Trajectory selection for an autonomous vehicle

ABSTRACT

A navigation system for a host vehicle may include at least one processor programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle. The processor may also be programmed to analyze at least one of the plurality of images to identify navigational state information associated with the host vehicle; determine a plurality of potential trajectories for the host vehicle based on the navigational state information; perform a preliminary analysis relative to each of the plurality of potential trajectories and assign to each of the plurality of potential trajectories, based on the preliminary analysis, at least one indicator of relative ranking; select, based on the at least one indicator of relative ranking assigned to each of the plurality of potential trajectories, a subset of the plurality of potential trajectories, wherein the subset of the plurality of potential trajectories includes fewer potential trajectories than the plurality of potential trajectories; perform a secondary analysis relative to the subset of the plurality of potential trajectories, and based on the secondary analysis, select one of the subset of the plurality of potential trajectories as a planned trajectory for the host vehicle; determine one or more navigational actions for the host vehicle based on the planned trajectory selected from among the subset of the plurality of potential trajectories; and cause at least one adjustment of a navigational actuator of the host vehicle to implement the one or more navigational actions for the host vehicle.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/473,643, filed on Mar. 20, 2017. The foregoingapplication is incorporated herein by reference in its entirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera), information from radar or lidar,and may also use information obtained from other sources (e.g., from aGPS device, a speed sensor, an accelerometer, a suspension sensor,etc.). At the same time, in order to navigate to a destination, anautonomous vehicle may also need to identify its location within aparticular roadway (e.g., a specific lane within a multi-lane road),navigate alongside other vehicles, avoid obstacles and pedestrians,observe traffic signals and signs, travel from one road to another roadat appropriate intersections or interchanges, and respond to any othersituation that occurs or develops during the vehicle's operation.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras. The navigational response mayalso take into account other data including, for example, globalpositioning system (GPS) data, sensor data (e.g., from an accelerometer,a speed sensor, a suspension sensor, etc.), and/or other map data.

In some embodiments, a navigation system for a host vehicle may includeat least one processor programmed to receive, from a camera, a pluralityof images representative of an environment of the host vehicle. Theprocessor may also be programmed to analyze at least one of theplurality of images to identify navigational state informationassociated with the host vehicle; determine a plurality of potentialtrajectories for the host vehicle based on the navigational stateinformation; perform a preliminary analysis relative to each of theplurality of potential trajectories and assign to each of the pluralityof potential trajectories, based on the preliminary analysis, at leastone indicator of relative ranking; select, based on the at least oneindicator of relative ranking assigned to each of the plurality ofpotential trajectories, a subset of the plurality of potentialtrajectories, wherein the subset of the plurality of potentialtrajectories includes fewer potential trajectories than the plurality ofpotential trajectories; perform a secondary analysis relative to thesubset of the plurality of potential trajectories, and based on thesecondary analysis, select one of the subset of the plurality ofpotential trajectories as a planned trajectory for the host vehicle;determine one or more navigational actions for the host vehicle based onthe planned trajectory selected from among the subset of the pluralityof potential trajectories; and cause at least one adjustment of anavigational actuator of the host vehicle to implement the one or morenavigational actions for the host vehicle.

In some embodiments, an autonomous vehicle may include a body; a cameraconfigured to capture a plurality of images representative of anenvironment of the autonomous vehicle; and at least one processorprogrammed to: receive from the camera the plurality of captured images;analyze at least one of the plurality of images to identify navigationalstate information associated with the autonomous vehicle; determine aplurality of potential trajectories for the autonomous vehicle based onthe navigational state information; perform a preliminary analysisrelative to each of the plurality of potential trajectories and assignto each of the plurality of potential trajectories, based on thepreliminary analysis, at least one indicator of relative ranking;select, based on the at least one indicator of relative ranking assignedto each of the plurality of potential trajectories, a subset of theplurality of potential trajectories, wherein the subset of the pluralityof potential trajectories includes fewer potential trajectories than theplurality of potential trajectories; perform a secondary analysisrelative to the subset of the plurality of potential trajectories, andbased on the secondary analysis, select one of the subset of theplurality of potential trajectories as a planned trajectory for theautonomous vehicle; determine one or more navigational actions for theautonomous vehicle based on the planned trajectory selected from amongthe subset of the plurality of potential trajectories; and cause atleast one adjustment of a navigational actuator of the autonomousvehicle to implement the one or more navigational actions for theautonomous vehicle.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is a block diagram representation of modules that may beimplemented by one or more specifically programmed processing devices ofa navigation system for an autonomous vehicle consistent with thedisclosed embodiments.

FIG. 9 is a navigation options graph consistent with the disclosedembodiments.

FIG. 10 is a navigation options graph consistent with the disclosedembodiments.

FIGS. 11A, 11B, and 11C provide a schematic representation ofnavigational options of a host vehicle in a merge zone consistent withthe disclosed embodiments.

FIG. 11D provide a diagrammatic depiction of a double merge scenario.

FIG. 11E provides an options graph potentially useful in a double mergescenario.

FIG. 12 provides a diagram of a representative image captured of anenvironment of a host vehicle, along with potential navigationalconstraints consistent with the disclosed embodiments.

FIG. 13 provides an algorithmic flow chart for navigating a vehicleconsistent with the disclosed embodiments.

FIG. 14 provides an algorithmic flow chart for navigating a vehicleconsistent with the disclosed embodiments.

FIG. 15 provides an algorithmic flow chart for navigating a vehicleconsistent with the disclosed embodiments.

FIG. 16 provides an algorithmic flow chart for navigating a vehicleconsistent with the disclosed embodiments.

FIGS. 17A and 17B provide a diagrammatic illustration of a host vehiclenavigating into a roundabout consistent with the disclosed embodiments.

FIG. 18 provides an algorithmic flow chart for navigating a vehicleconsistent with the disclosed embodiments.

FIG. 19 provides a block diagram representation of a system architectureconsistent with exemplary disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration/deceleration of thevehicle. To be autonomous, a vehicle need not be fully automatic (e.g.,fully operational without a driver or without driver input). Rather, anautonomous vehicle includes those that can operate under driver controlduring certain time periods and without driver control during other timeperiods. Autonomous vehicles may also include vehicles that control onlysome aspects of vehicle navigation, such as steering (e.g., to maintaina vehicle course between vehicle lane constraints) or some steeringoperations under certain circumstances (but not under allcircumstances), but may leave other aspects to the driver (e.g., brakingor braking under certain circumstances). In some cases, autonomousvehicles may handle some or all aspects of braking, speed control,and/or steering of the vehicle.

As human drivers typically rely on visual cues and observations in orderto control a vehicle, transportation infrastructures are builtaccordingly, with lane markings, traffic signs, and traffic lightsdesigned to provide visual information to drivers. In view of thesedesign characteristics of transportation infrastructures, an autonomousvehicle may include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, images representing components ofthe transportation infrastructure (e.g., lane markings, traffic signs,traffic lights, etc.) that are observable by drivers and other obstacles(e.g., other vehicles, pedestrians, debris, etc.). Additionally, anautonomous vehicle may also use stored information, such as informationthat provides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while it istraveling, and the vehicle (as well as other vehicles) may use theinformation to localize itself on the model. Some vehicles can also becapable of communication among them, sharing information, altering thepeer vehicle of hazards or changes in the vehicles' surroundings, etc.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras, CCDs, or any other type of image sensor), such as imagecapture device 122, image capture device 124, and image capture device126. System 100 may also include a data interface 128 communicativelyconnecting processing unit 110 to image acquisition unit 120. Forexample, data interface 128 may include any wired and/or wireless linkor links for transmitting image data acquired by image acquisition unit120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of hardware-based processing devices. For example, eitheror both of applications processor 180 and image processor 190 mayinclude a microprocessor, preprocessors (such as an image preprocessor),graphics processors, a central processing unit (CPU), support circuits,digital signal processors, integrated circuits, memory, or any othertypes of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc. andmay include various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be usedin the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation. In either case, the processing deviceconfigured to perform the sensing, image analysis, and/or navigationalfunctions disclosed herein represents a specialized hardware-basedsystem in control of multiple hardware based components of a hostvehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory, read only memory, flash memory, disk drives, opticalstorage, tape storage, removable storage and/or any other types ofstorage. In some embodiments, memory units 140, 150 may be separate fromthe applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a speedometer) for measuring a speed of vehicle 200.System 100 may also include one or more accelerometers (either singleaxis or multiaxis) for measuring accelerations of vehicle 200 along oneor more axes.

The memory units 140, 150 may include a database, or data organized inany other form, that indication a location of known landmarks. Sensoryinformation (such as images, radar signal, depth information from lidaror stereo processing of two or more images) of the environment may beprocessed together with position information, such as a GPS coordinate,vehicle's ego motion, etc. to determine a current location of thevehicle relative to the known landmarks, and refine the vehiclelocation. Certain aspects of this technology are included in alocalization technology known as REM′, which is being marketed by theassignee of the present application.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle. Mapdatabase 160 may also include stored representations of variousrecognized landmarks that may be used to determine or update a knownposition of the host vehicle with respect to a target trajectory. Thelandmark representations may include data fields such as landmark type,landmark location, among other potential identifiers.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

One or more cameras (e.g., image capture devices 122, 124, and 126) maybe part of a sensing block included on a vehicle. Various other sensorsmay be included in the sensing block, and any or all of the sensors maybe relied upon to develop a sensed navigational state of the vehicle. Inaddition to cameras (forward, sideward, rearward, etc), other sensorssuch as RADAR, LIDAR, and acoustic sensors may be included in thesensing block. Additionally, the sensing block may include one or morecomponents configured to communicate and transmit/receive informationrelating to the environment of the vehicle. For example, such componentsmay include wireless transceivers (RF, etc.) that may receive from asource remotely located with respect to the host vehicle sensor basedinformation or any other type of information relating to the environmentof the host vehicle. Such information may include sensor outputinformation, or related information, received from vehicle systems otherthan the host vehicle. In some embodiments, such information may includeinformation received from a remote computing device, a centralizedserver, etc. Furthermore, the cameras may take on many differentconfigurations: single camera units, multiple cameras, camera clusters,long FOV, short FOV, wide angle, fisheye, etc.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light fixtures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead transmit data,such as captured images and/or limited location information related to aroute.

Other privacy levels are contemplated as well. For example, system 100may transmit data to a server according to an “intermediate” privacylevel and include additional information not included under a “high”privacy level, such as a make and/or model of a vehicle and/or a vehicletype (e.g., a passenger vehicle, sport utility vehicle, truck, etc.). Insome embodiments, system 100 may upload data according to a “low”privacy level. Under a “low” privacy level setting, system 100 mayupload data and include information sufficient to uniquely identify aspecific vehicle, owner/driver, and/or a portion or entirely of a routetraveled by the vehicle. Such “low” privacy level data may include oneor more of, for example, a VIN, a driver/owner name, an originationpoint of a vehicle prior to departure, an intended destination of thevehicle, a make and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with vehicle 200. Each ofthe plurality of second and third images may be acquired as a second andthird series of image scan lines, which may be captured using a rollingshutter. Each scan line or row may have a plurality of pixels. Imagecapture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). Other camera configurations are consistentwith the disclosed embodiments, and the configurations disclosed hereinare examples. For example, system 100 may include a configuration of anynumber of cameras (e.g., one, two, three, four, five, six, seven, eight,etc.) Furthermore, system 100 may include “clusters” of cameras. Forexample, a cluster of cameras (including any appropriate number ofcameras, e.g., one, four, eight, etc.) may be forward-facing relative toa vehicle, or may be facing any other direction (e.g., reward-facing,side-facing, at an angle, etc.) Accordingly, system 100 may includemultiple clusters of cameras, with each cluster oriented in a particulardirection to capture images from a particular region of a vehicle'senvironment.

The first camera may have a field of view that is greater than, lessthan, or partially overlapping with, the field of view of the secondcamera. In addition, the first camera may be connected to a first imageprocessor to perform monocular image analysis of images provided by thefirst camera, and the second camera may be connected to a second imageprocessor to perform monocular image analysis of images provided by thesecond camera. The outputs (e.g., processed information) of the firstand second image processors may be combined. In some embodiments, thesecond image processor may receive images from both the first camera andsecond camera to perform stereo analysis. In another embodiment, system100 may use a three-camera imaging system where each of the cameras hasa different field of view. Such a system may, therefore, make decisionsbased on information derived from objects located at varying distancesboth forward and to the sides of the vehicle. References to monocularimage analysis may refer to instances where image analysis is performedbased on images captured from a single point of view (e.g., from asingle camera). Stereo image analysis may refer to instances where imageanalysis is performed based on two or more images captured with one ormore variations of an image capture parameter. For example, capturedimages suitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from the main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, into which may be stored instructions for performing one or moreoperations consistent with the disclosed embodiments. Although thefollowing refers to memory 140, one of skill in the art will recognizethat instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, applications processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto applications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with determining a navigational response.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(i), z_(i))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights. Furthermore, in some embodiments,the analysis may make use of trained system (e.g., a machine learning ordeep learning system), which may, for example, estimate a future pathahead of a current location of a vehicle based on an image captured atthe current location.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing in the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Reinforcement Learning and Trained Navigational Systems

The sections that follow discuss autonomous driving along with systemsand methods for accomplishing autonomous control of a vehicle, whetherthat control is fully autonomous (a self-driving vehicle) or partiallyautonomous (e.g., one or more driver assist systems or functions). Asshown in FIG. 8, the autonomous driving task can be partitioned intothree main modules, including a sensing module 801, a driving policymodule 803, and a control module 805. In some embodiments, modules 801,803, and 805 may be stored in memory unit 140 and/or memory unit 150 ofsystem 100, or modules 801, 803, and 805 (or portions thereof) may bestored remotely from system 100 (e.g., stored in a server accessible tosystem 100 via, for example, wireless transceiver 172). Furthermore, anyof the modules (e.g., modules 801, 803, and 805) disclosed herein mayimplement techniques associated with a trained system (such as a neuralnetwork or a deep neural network) or an untrained system.

Sensing module 801, which may be implemented using processing unit 110,may handle various tasks relating to sensing of a navigational state inan environment of a host vehicle. Such tasks may rely upon input fromvarious sensors and sensing systems associated with the host vehicle.These inputs may include images or image streams from one or moreonboard cameras, GPS position information, accelerometer outputs, userfeedback, or user inputs to one or more user interface devices, radar,lidar, etc. Sensing, which may include data from cameras and/or anyother available sensors, along with map information, may be collected,analyzed, and formulated into a “sensed state,” describing informationextracted from a scene in the environment of the host vehicle. Thesensed state may include sensed information relating to target vehicles,lane markings, pedestrians, traffic lights, road geometry, lane shape,obstacles, distances to other objects/vehicles, relative velocities,relative accelerations, among any other potential sensed information.Supervised machine learning may be implemented in order to produce asensing state output based on sensed data provided to sensing module801. The output of the sensing module may represent a sensednavigational “state” of the host vehicle, which may be passed to drivingpolicy module 803.

While a sensed state may be developed based on image data received fromone or more cameras or image sensors associated with a host vehicle, asensed state for use in navigation may be developed using any suitablesensor or combination of sensors. In some embodiments, the sensed statemay be developed without reliance upon captured image data. In fact, anyof the navigational principles described herein may be applicable tosensed states developed based on captured image data as well as sensedstates developed using other non-image based sensors. The sensed statemay also be determined via sources external to the host vehicle. Forexample, a sensed state may be developed in full or in part based oninformation received from sources remote from the host vehicle (e.g.,based on sensor information, processed state information, etc. sharedfrom other vehicles, shared from a central server, or from any othersource of information relevant to a navigational state of the hostvehicle.)

Driving policy module 803, which is discussed in more detail below andwhich may be implemented using processing unit 110, may implement adesired driving policy in order to decide on one or more navigationalactions for the host vehicle to take in response to the sensednavigational state. If there are no other agents (e.g., target vehiclesor pedestrians) present in the environment of the host vehicle, thesensed state input to driving policy module 803 may be handled in arelatively straightforward manner. The task becomes more complex whenthe sensed state requires negotiation with one or more other agents. Thetechnology used to generate the output of driving policy module 803 mayinclude reinforcement learning (discussed in more detail below). Theoutput of driving policy module 803 may include at least onenavigational action for the host vehicle and may include a desiredacceleration (which may translate to an updated speed for the hostvehicle), a desired yaw rate for the host vehicle, a desired trajectory,among other potential desired navigational actions.

Based on the output from the driving policy module 803, control module805, which may also be implemented using processing unit 110, maydevelop control instructions for one or more actuators or controlleddevices associated with the host vehicle. Such actuators and devices mayinclude an accelerator, one or more steering controls, a brake, a signaltransmitter, a display, or any other actuator or device that may becontrolled as part of a navigation operation associated with a hostvehicle. Aspects of control theory may be used to generate the output ofcontrol module 805. Control module 805 may be responsible for developingand outputting instructions to controllable components of the hostvehicle in order to implement the desired navigational goals orrequirements of driving policy module 803.

Returning to driving policy module 803, in some embodiments, a trainedsystem trained through reinforcement learning may be used to implementdriving policy module 803. In other embodiments, driving policy module803 may be implemented without a machine learning approach, by usingspecified algorithms to “manually” address the various scenarios thatmay arise during autonomous navigation. Such an approach, however, whileviable, may result in a driving policy that is too simplistic and maylack the flexibility of a trained system based on machine learning. Atrained system, for example, may be better equipped to handle complexnavigational states and may better determine whether a taxi is parkingor is stopping to pick up or drop off a passenger; determine whether apedestrian intends to cross the street ahead of the host vehicle;balance unexpected behavior of other drivers with defensiveness;negotiate in dense traffic involving target vehicles and/or pedestrians;decide when to suspend certain navigational rules or augment otherrules; anticipate unsensed, but anticipated conditions (e.g., whether apedestrian will emerge from behind a car or obstacle); etc. A trainedsystem based on reinforcement learning may also be better equipped toaddress a state space that is continuous and high-dimensional along withan action space that is continuous.

Training of the system using reinforcement learning may involve learninga driving policy in order to map from sensed states to navigationalactions. A driving policy is a function π: S→A, where S is a set ofstates and A⊂

² is the action space (e.g., desired speed, acceleration, yaw commands,etc.). The state space is S=S_(s)×S_(p), where S_(s) is the sensingstate and S_(p) is additional information on the state saved by thepolicy. Working in discrete time intervals, at time t, the current states_(t)∈S may be observed, and the policy may be applied to obtain adesired action, a_(t)=π(s_(t)).

The system may be trained through exposure to various navigationalstates, having the system apply the policy, providing a reward (based ona reward function designed to reward desirable navigational behavior).Based on the reward feedback, the system may “learn” the policy andbecomes trained in producing desirable navigational actions. Forexample, the learning system may observe the current state s_(t)∈S anddecide on an action a_(t)∈A based on a policy π: S→

(A). Based on the decided action (and implementation of the action), theenvironment moves to the next state s_(t+1)∈S for observation by thelearning system. For each action developed in response to the observedstate, the feedback to the learning system is a reward signal r₁, r₂, .. . .

The goal of Reinforcement Learning (RL) is to find a policy π. It isusually assumed that at time t, there is a reward function r_(t) whichmeasures the instantaneous quality of being at state s_(t) and takingaction a_(t). However, taking the action a_(t) at time t affects theenvironment and therefore affects the value of the future states. As aresult, when deciding on what action to take, not only should thecurrent reward be taken into account, but future rewards should also beconsidered. In some instances the system should take a certain action,even though it is associated with a reward lower than another availableoption, when the system determines that in the future a greater rewardmay be realized if the lower reward option is taken now. To formalizethis, observe that a policy, π, and an initial state, s, induces adistribution over

^(T), where the probability of a vector (r₁, . . . , r_(T)) is theprobability of observing the rewards r₁, . . . , r_(T), if the agentstarts at state s₀=s and from there on follows the policy π. The valueof the initial state s may be defined as:

$\left. {{{V^{\pi}(s)} = {{\left\lbrack {\sum\limits_{t = 1}^{T}r_{t}} \right.s_{0}} = s}},{\forall{t \geq 1}},{a_{t} = {\pi\left( s_{t} \right)}}} \right\rbrack.$

Instead of restricting the time horizon to T, the future rewards may bediscounted to define, for some fixed γ∈(0,1):

$\left. {{{V^{\pi}(s)} = {{\left\lbrack {\sum\limits_{t = 1}^{\infty}{\gamma^{t}r_{t}}} \right.s_{0}} = s}},{\forall{t \geq 1}},{a_{t} = {\pi\left( s_{t} \right)}}} \right\rbrack.$

In any case, the optimal policy is the solution of

$\underset{\pi}{\arg\;\max}\;{\left\lbrack {V^{\pi}(s)} \right\rbrack}$

where the expectation is over the initial state, s.

There are several possible methodologies for training the driving policysystem. For example, an imitation approach (e.g., behavior cloning) maybe used in which the system learns from state/action pairs where theactions are those that would be chosen by a good agent (e.g., a human)in response to a particular observed state. Suppose a human driver isobserved. Through this observation, many examples of the form (s_(t),a_(t)), where s_(t) is the state and a_(t) is the action of the humandriver could be obtained, observed, and used as a basis for training thedriving policy system. For example, supervised learning can be used tolearn a policy it such that π(s_(t))≈a_(t). There are many potentialadvantages of this approach. First, there is no requirement to define areward function. Second, the learning is supervised and happens offline(there is no need to apply the agent in the learning process). Adisadvantage of this method is that different human drivers, and eventhe same human drivers, are not deterministic in their policy choices.Hence, learning a function for which ∥π(s_(t))−a_(t)∥ is very small isoften infeasible. And, even small errors may accumulate over time toyield large errors.

Another technique that may be employed is policy based learning. Here,the policy may be expressed in parametric form and directly optimizedusing a suitable optimization technique (e.g., stochastic gradientdescent). The approach is to directly solve the problem given in

$\underset{\pi}{\arg\;\max}\;{{\left\lbrack {V^{\pi}(s)} \right\rbrack}.}$

There are of course many ways to solve the problem. One advantage ofthis approach is that it tackles the problem directly, and thereforeoften leads to good practical results. One potential disadvantage isthat it often requires an “on-policy” training, namely, the learning ofπ is an iterative process, where at iteration j we have a non-perfectpolicy, π_(j), and to construct the next policy π_(j), we must interactwith the environment while acting based on π_(j).

The system may also be trained through value based learning (learning Qor V functions). Suppose a good approximation can be learned to theoptimal value function V*. An optimal policy may be constructed (e.g.,by relying on the Bellman equation). Some versions of value basedlearning can be implemented offline (called “off-policy” training). Somedisadvantages of the value-based approach may result from its strongdependence on Markovian assumptions and required approximation of acomplicated function (it may be more difficult to approximate the valuefunction than to approximate the policy directly).

Another technique may include model based learning and planning(learning the probability of state transitions and solving theoptimization problem of finding the optimal V). Combinations of thesetechniques may also be used to train the learning system. In thisapproach, the dynamics of the process may be learned, namely, thefunction that takes (s_(t), a_(t)) and yields a distribution over thenext state s_(t+1). Once this function is learned, the optimizationproblem may be solved to find the policy π whose value is optimal. Thisis called “planning” One advantage of this approach may be that thelearning part is supervised and can be applied offline by observingtriplets (s_(t), a_(t), s_(t+1)). One disadvantage of this approach,similar to the “imitation” approach, may be that small errors in thelearning process can accumulate and to yield inadequately performingpolicies.

Another approach for training driving policy module 803 may includedecomposing the driving policy function into semantically meaningfulcomponents. This allows implementation of parts of the policy manually,which may ensure the safety of the policy, and implementation of otherparts of the policy using reinforcement learning techniques, which mayenable adaptivity to many scenarios, a human-like balance betweendefensive/aggressive behavior, and a human-like negotiation with otherdrivers. From the technical perspective, a reinforcement learningapproach may combine several methodologies and offer a tractabletraining procedure, where most of the training can be performed usingeither recorded data or a self-constructed simulator.

In some embodiments, training of driving policy module 803 may rely uponan “options” mechanism. To illustrate, consider a simple scenario of adriving policy for a two-lane highway. In a direct RL approach, a policyπ that maps the state into A⊂

², where the first component of π (s) is the desired accelerationcommand and the second component of π (s) is the yaw rate. In a modifiedapproach, the following policies can be constructed:

Automatic Cruise Control (ACC) policy, o_(ACC): S→A: this policy alwaysoutputs a yaw rate of 0 and only changes the speed so as to implementsmooth and accident-free driving.

ACC+Left policy, o_(L): S→A: the longitudinal command of this policy isthe same as the ACC command. The yaw rate is a straightforwardimplementation of centering the vehicle toward the middle of the leftlane, while ensuring a safe lateral movement (e.g., don't move left ifthere's a car on the left side).

ACC+Right policy, o_(R): S→A: Same as o_(L), but the vehicle may becentered toward the middle of the right lane.

These policies may be referred to as “options”. Relying on these“options”, a policy can be learned that selects options, π_(o): S→O,where O is the set of available options. In one case,O={o_(ACC),o_(L),o_(R)}. The option-selector policy, π_(o), defines anactual policy, π: S→A, by setting, for every s, π(s)=o_(π) _(o)_((s))(s).

In practice, the policy function may be decomposed into an options graph901, as shown in FIG. 9. Another example options graph 1000 is shown inFIG. 10. The options graph can represent a hierarchical set of decisionsorganized as a Directed Acyclic Graph (DAG). There is a special nodecalled the root node 903 of the graph. This node has no incoming nodes.The decision process traverses through the graph, starting from the rootnode, until it reaches a “leaf” node, which refers to a node that has nooutgoing decision lines. As shown in FIG. 9, leaf nodes may includenodes 905, 907, and 909, for example. Upon encountering a leaf node,driving policy module 803 may output the acceleration and steeringcommands associated with a desired navigational action associated withthe leaf node.

Internal nodes, such as nodes 911, 913, and 915, for example, may resultin implementation of a policy that chooses a child among its availableoptions. The set of available children of an internal node include allof the nodes associated with a particular internal node via decisionlines. For example, internal node 913 designated as “Merge” in FIG. 9includes three children nodes 909, 915, and 917 (“Stay,” “OvertakeRight,” and “Overtake Left,” respectively) each joined to node 913 by adecision line.

Flexibility of the decision making system may be gained by enablingnodes to adjust their position in the hierarchy of the options graph.For example, any of the nodes may be allowed to declare themselves as“critical.” Each node may implement a function “is critical,” thatoutputs “True” if the node is in a critical section of its policyimplementation. For example, a node that is responsible for a take-over,may declare itself as critical while in the middle of a maneuver. Thismay impose constraints on the set of available children of a node u,which may include all nodes v which are children of node u and for whichthere exists a path from v to a leaf node that goes through all nodesdesignated as critical. Such an approach may allow, on one hand,declaration of the desired path on the graph at each time step, while onthe other hand, stability of a policy may be preserved, especially whilecritical portions of the policy are being implemented.

By defining an options graph, the problem of learning the driving policyπ: S→A may be decomposed into a problem of defining a policy for eachnode of the graph, where the policy at internal nodes should choose fromamong available children nodes. For some of the nodes, the respectivepolicy may be implemented manually (e.g., through if-then typealgorithms specifying a set of actions in response to an observed state)while for others the policies may be implemented using a trained systembuilt through reinforcement learning. The choice between manual ortrained/learned approaches may depend on safety aspects associated withthe task and on its relative simplicity. The option graphs may beconstructed in a manner such that some of the nodes are straightforwardto implement, while other nodes may rely on trained models. Such anapproach can ensure safe operation of the system.

The following discussion provides further details regarding the role ofthe options graph of FIG. 9 within driving policy module 803. Asdiscussed above, the input to the driving policy module is a “sensedstate,” which summarizes the environment map, for example, as obtainedfrom available sensors. The output of driving policy module 803 is a setof desires (optionally, together with a set of hard constraints) thatdefine a trajectory as a solution of an optimization problem.

As described above, the options graph represents a hierarchical set ofdecisions organized as a DAG. There is a special node called the “root”of the graph. The root node is the only node that has no incoming edges(e.g., decision lines). The decision process traverses the graph,starting from the root node, until it reaches a “leaf” node, namely, anode that has no outgoing edges. Each internal node should implement apolicy that picks a child among its available children. Every leaf nodeshould implement a policy that, based on the entire path from the rootto the leaf, defines a set of Desires (e.g., a set of navigational goalsfor the host vehicle). The set of Desires, together with a set of hardconstraints that are defined directly based on the sensed state,establish an optimization problem whose solution is the trajectory forthe vehicle. The hard constraints may be employed to further increasethe safety of the system, and the Desires can be used to provide drivingcomfort and human-like driving behavior of the system. The trajectoryprovided as a solution to the optimization problem, in turn, defines thecommands that should be provided to the steering, braking, and/or engineactuators in order to accomplish the trajectory.

Returning to FIG. 9, options graph 901 represents an options graph for atwo-lane highway, including with merging lanes (meaning that at somepoints, a third lane is merged into either the right or the left lane ofthe highway). The root node 903 first decides if the host vehicle is ina plain road scenario or approaching a merge scenario. This is anexample of a decision that can be implemented based on the sensingstate. Plain road node 911 includes three child nodes: stay node 909,overtake left node 917, and overtake right node 915. Stay refers to asituation in which the host vehicle would like to keep driving in thesame lane. The stay node is a leaf node (no outgoing edges/lines).Therefore, it the stay node defines a set of Desires. The first Desireit defines may include the desired lateral position—e.g., as close aspossible to the center of the current lane of travel. There may also bea desire to navigate smoothly (e.g., within predefined or allowableacceleration maximums). The stay node may also define how the hostvehicle is to react to other vehicles. For example, the stay node mayreview sensed target vehicles and assign each a semantic meaning, whichcan be translated into components of the trajectory.

Various semantic meanings may be assigned to target vehicles in anenvironment of the host vehicle. For example, in some embodiments thesemantic meaning may include any of the following designations: 1) notrelevant: indicating that the sensed vehicle in the scene is currentlynot relevant; 2) next lane: indicating that the sensed vehicle is in anadjacent lane and an appropriate offset should be maintained relative tothis vehicle (the exact offset may be calculated in the optimizationproblem that constructs the trajectory given the Desires and hardconstraints, and can potentially be vehicle dependent—the stay leaf ofthe options graph sets the target vehicle's semantic type, which definesthe Desire relative to the target vehicle); 3) give way: the hostvehicle will attempt to give way to the sensed target vehicle by, forexample, reducing speed (especially where the host vehicle determinesthat the target vehicle is likely to cut into the lane of the hostvehicle); 4) take way: the host vehicle will attempt to take the rightof way by, for example, increasing speed; 5) follow: the host vehicledesires to maintain smooth driving following after this target vehicle;6) takeover left/right: this means the host vehicle would like toinitiate a lane change to the left or right lane. Overtake left node 917and overtake right node 915 are internal nodes that do not yet defineDesires.

The next node in options graph 901 is the select gap node 919. This nodemay be responsible for selecting a gap between two target vehicles in aparticular target lane that host vehicle desires to enter. By choosing anode of the form IDj, for some value of j, the host vehicle arrives at aleaf that designates a Desire for the trajectory optimizationproblem—e.g., the host vehicle wishes to make a maneuver so as to arriveat the selected gap. Such a maneuver may involve firstaccelerating/braking in the current lane and then heading to the targetlane at an appropriate time to enter the selected gap. If the select gapnode 919 cannot find an appropriate gap, it moves to the abort node 921,which defines a desire to move back to the center of the current laneand cancel the takeover.

Returning to merge node 913, when the host vehicle approaches a merge,it has several options that may depend on a particular situation. Forexample, as shown in FIG. 11A, host vehicle 1105 is traveling along atwo-lane road with no other target vehicles detected, either in theprimary lanes of the two-lane road or in the merge lane 1111. In thissituation, driving policy module 803, upon reaching merge node 913, mayselect stay node 909. That is, staying within its current lane may bedesired where no target vehicles are sensed as merging onto the roadway.

In FIG. 11B, the situation is slightly different. Here, host vehicle1105 senses one or more target vehicles 1107 entering the main roadway1112 from merge lane 1111. In this situation, once driving policy module803 encounters merge node 913, it may choose to initiate an overtakeleft maneuver in order to avoid the merging situation.

In FIG. 11C, host vehicle 1105 encounters one or more target vehicles1107 entering main roadway 1112 from merge lane 1111. Host vehicle 1105also detects target vehicles 1109 traveling in a lane adjacent to thelane of the host vehicle. The host vehicle also detects one or moretarget vehicles 1110 traveling in the same lane as host vehicle 1105. Inthis situation, driving policy module 803 may decide to adjust the speedof host vehicle 1105 to give way to target vehicle 1107 and to proceedahead of target vehicle 1115. This can be accomplished, for example, byprogressing to select gap node 919, which, in turn, will select a gapbetween ID0 (vehicle 1107) and ID1 (vehicle 1115) as the appropriatemerging gap. In such a case, the appropriate gap of the mergingsituation defines the objective for a trajectory planner optimizationproblem.

As discussed above, nodes of the options graph may declare themselves as“critical,” which may ensure that the selected option passes through thecritical nodes. Formally, each node may implement a function IsCritical.After performing a forward pass on the options graph, from the root to aleaf, and solving the optimization problem of the trajectory planner, abackward pass may be performed from the leaf back to the root. Alongthis backward pass, the IsCritical function of all nodes in the pass maybe called, and a list of all critical nodes may be saved. In the forwardpath corresponding to the next time frame, driving policy module 803 maybe required to choose a path from the root node to a leaf that goesthrough all critical nodes.

FIGS. 11A-11C may be used to show a potential benefit of this approach.For example, in a situation where an overtake action is initiated, anddriving policy module 803 arrives at the leaf corresponding to IDk, itwould be undesirable to choose, for example, the stay node 909 when thehost vehicle is in the middle of the takeover maneuver. To avoid suchjumpiness, the IDj node can designate itself as critical. During themaneuver, the success of the trajectory planner can be monitored, andfunction IsCritical will return a “True” value if the overtake maneuverprogresses as intended. This approach may ensure that in the next timeframe, the takeover maneuver will be continued (rather than jumping toanother, potentially inconsistent maneuver prior to completion of theinitially selected maneuver). If, on the other hand, monitoring of themaneuver indicates that the selected maneuver is not progressing asintended, or if the maneuver has become unnecessary or impossible, thefunction IsCritical can return a “False” value. This can allow theselect gap node to select a different gap in the next time frame, or toabort the overtake maneuver altogether. This approach may allow, on onehand, declaration of the desired path on the options graph at each timestep, while on the other hand, may help to promote stability of thepolicy while in critical parts of the execution.

Hard constraints, which will be discussed in more detail below, may bedifferentiated from navigational desires. For example, hard constraintsmay ensure safe driving by applying an added layer of filtering of aplanned navigational action. The implicated hard constraints, which maybe programmed and defined manually, rather than through use of a trainedsystem built upon reinforcement learning, can be determined from thesensed state. In some embodiments, however, the trained system may learnthe applicable hard constraints to be applied and followed. Such anapproach may promote driving policy module 803 arriving at a selectedaction that is already in compliance with the applicable hardconstraints, which may reduce or eliminate selected actions that mayrequire later modification to comply with applicable hard constraints.Nevertheless, as a redundant safety measure, hard constraints may beapplied to the output of driving policy module 803 even where drivingpolicy module 803 has been trained to account for predetermined hardconstraints.

There are many examples of potential hard constraints. For example, ahard constraint may be defined in conjunction with a guardrail on anedge of a road. In no situation may the host vehicle be allowed to passthe guardrail. Such a rule induces a hard lateral constraint on thetrajectory of the host vehicle. Another example of a hard constraint mayinclude a road bump (e.g., a speed control bump), which may induce ahard constraint on the speed of driving before the bump and whiletraversing the bump. Hard constraints may be considered safety criticaland, therefore, may be defined manually rather than relying solely on atrained system learning the constraints during training.

In contrast to hard constraints, the goal of desires may be to enable orachieve comfortable driving. As discussed above, an example of a desiremay include a goal of positioning the host vehicle at a lateral positionwithin a lane that corresponds to the center of the host vehicle lane.Another desire may include the ID of a gap to fit into. Note that thereis not a requirement for the host vehicle to be exactly in the center ofthe lane, but instead a desire to be as close as possible to it mayensure that the host vehicle tends to migrate to the center of the laneeven in the event of deviations from the center of the lane. Desires maynot be safety critical. In some embodiments, desires may requirenegotiation with other drivers and pedestrians. One approach forconstructing the desires may rely on the options graph, and the policyimplemented in at least some nodes of the graph may be based onreinforcement learning.

For the nodes of options graph 901 or 1000 implemented as nodes trainedbased on learning, the training process may include decomposing theproblem into a supervised learning phase and a reinforcement learningphase. In the supervised learning phase, a differentiable mapping from(s_(t), a_(t)) to ŝ_(t+1) can be learned such that ŝ_(t+1)≈s_(t+1). Thismay be similar to “model-based” reinforcement learning. However, in theforward loop of the network, ŝ_(t+1) may be replaced by the actual valueof s_(t+1), therefore eliminating the problem of error accumulation. Therole of prediction of ŝ_(t+1) is to propagate messages from the futureback to past actions. In this sense, the algorithm may be a combinationof “model-based” reinforcement learning with “policy-based learning.”

An important element that may be provided in some scenarios is adifferentiable path from future losses/rewards back to decisions onactions. With the option graph structure, the implementation of optionsthat involve safety constraints are usually not differentiable. Toovercome this issue, the choice of a child in a learned policy node maybe stochastic. That is, a node may output a probability vector, p, thatassigns probabilities used in choosing each of the children of theparticular node. Suppose that a node has k children and let a⁽¹⁾, . . ., a^((k)) be the actions of the path from each child to a leaf. Theresulting predicted action is therefore â=Σ_(i=1) ^(k)p_(i)a^((i)),which may result in a differentiable path from the action to p. Inpractice, an action a may be chosen to be a^((i)) for i˜p, and thedifference between a and â may be referred to as additive noise.

For the training of ŝ_(t+1) given s_(t), a_(t), supervised learning maybe used together with real data. For training the policy of nodessimulators can be used. Later, fine tuning of a policy can beaccomplished using real data. Two concepts may make the simulation morerealistic. First, using imitation, an initial policy can be constructedusing the “behavior cloning” paradigm, using large real world data sets.In some cases, the resulting agents may be suitable. In other cases, theresulting agents at least form very good initial policies for the otheragents on the roads. Second, using self-play, our own policy may be usedto augment the training. For example, given an initial implementation ofthe other agents (cars/pedestrians) that may be experienced, a policymay be trained based on a simulator. Some of the other agents may bereplaced with the new policy, and the process may be repeated. As aresult, the policy can continue to improve as it should respond to alarger variety of other agents that have differing levels ofsophistication.

Further, in some embodiments, the system may implement a multi-agentapproach. For example, the system may take into account data fromvarious sources and/or images capturing from multiple angles. Further,some disclosed embodiments may provide economy of energy, asanticipation of an event which does not directly involve the hostvehicle, but which may have an effect on the host vehicle can beconsidered, or even anticipation of an event that may lead tounpredictable circumstances involving other vehicles may be aconsideration (e.g., radar may “see through” the leading vehicle andanticipation of an unavoidable, or even a high likelihood of an eventthat will affect the host vehicle).

Trained System with Imposed Navigational Constraints

In the context of autonomous driving, a significant concern is how toensure that a learned policy of a trained navigational network will besafe. In some embodiments, the driving policy system may be trainedusing constraints, such that the actions selected by the trained systemmay already account for applicable safety constraints. Additionally, insome embodiments, an extra layer of safety may be provided by passingthe selected actions of the trained system through one or more hardconstraints implicated by a particular sensed scene in the environmentof the host vehicle. Such an approach may ensure that that the actionstaken by the host vehicle have been restricted to those confirmed assatisfying applicable safety constraints.

At its core, the navigational system may include a learning algorithmbased on a policy function that maps an observed state to one or moredesired actions. In some implementations, the learning algorithm is adeep learning algorithm. The desired actions may include at least oneaction expected to maximize an anticipated reward for a vehicle. Whilein some cases, the actual action taken by the vehicle may correspond toone of the desired actions, in other cases, the actual action taken maybe determined based on the observed state, one or more desired actions,and non-learned, hard constraints (e.g., safety constraints) imposed onthe learning navigational engine. These constraints may include no-drivezones surrounding various types of detected objects (e.g., targetvehicles, pedestrians, stationary objects on the side of a road or in aroadway, moving objects on the side of a road or in a roadway, guardrails, etc.) In some cases, the size of the zone may vary based on adetected motion (e.g., speed and/or direction) of a detected object.Other constraints may include a maximum speed of travel when passingwithin an influence zone of a pedestrian, a maximum deceleration (toaccount for a target vehicle spacing behind the host vehicle), amandatory stop at a sensed crosswalk or railroad crossing, etc.

Hard constraints used in conjunction with a system trained throughmachine learning may offer a degree of safety in autonomous driving thatmay surpass a degree of safety available based on the output of thetrained system alone. For example, the machine learning system may betrained using a desired set of constraints as training guidelines and,therefore, the trained system may select an action in response to asensed navigational state that accounts for and adheres to thelimitations of applicable navigational constraints. Still, however, thetrained system has some flexibility in selecting navigational actionsand, therefore, there may exist at least some situations in which anaction selected by the trained system may not strictly adhere torelevant navigational constraints. Therefore, in order to require that aselected action strictly adheres to relevant navigational constraints,the output of the trained system may be combined with, compared to,filtered with, adjusted, modified, etc. using a non-machine learningcomponent outside the learning/trained framework that guarantees strictapplication of relevant navigational constraints.

The following discussion provides additional details regarding thetrained system and the potential benefits (especially from a safetyperspective) that may be gleaned from combining a trained system with analgorithmic component outside of the trained/learning framework. Asdiscussed, the reinforcement learning objective by policy may beoptimized through stochastic gradient ascent. The objective (e.g., theexpected reward) may be defined as E_(k˜Pa)R(s).

Objectives that involve expectation may be used in machine learningscenarios. Such an objective, without being bound by navigationalconstraints, however, may not return actions strictly bound by thoseconstraints. For example, considering a reward function for whichR(s)=−r for trajectories that represent a rare “corner” event to beavoided (e.g., such as an accident), and R(s)∈[−1,1] for the rest of thetrajectories, one goal for the learning system may be to learn toperform an overtake maneuver. Normally, in an accident free trajectory,R(s) would reward successful, smooth, takeovers and penalize staying ina lane without completing the takeover—hence the range [−1, 1]. If asequence, s, represents an accident, the reward, −r, should provide asufficiently high penalty to discourage such an occurrence. The questionis what should be the value of r to ensure accident-free driving.

Observe that the effect of an accident on

[R(s)] is the additive term −pr where p is the probability mass oftrajectories with an accident event. If this term is negligible, i.e.,p<<1/r, then the learning system may prefer a policy that performs anaccident (or adopt in general a reckless driving policy) in order tofulfill the takeover maneuver successfully more often than a policy thatwould be more defensive at the expense of having some takeover maneuversnot complete successfully. In other words, if the probability ofaccidents is to be at most p, then r must be set such that r>>1/p. Itmay be desireable to make p extremely small (e.g., on the order ofp=10⁻⁹). Therefore, r should be large. In policy gradient, the gradientof

[R(s)] may be estimated. The following lemma shows that the variance ofthe random variable R(s) grows with pr², which is larger than r forr>>1/p. Therefore, estimating the objective may be difficult, andestimating its gradient may be even more difficult.

Lemma: Let π_(o) be a policy and let p and r be scalars such that withprobability p, R(s)=−r is obtained, and with probability 1−p we haveR(s)∈[−1, 1] is obtained. Then,

Var[R( s )]≥pr ²−(pr+(1−p))²=(p−p ²)r ²−2p(1−p)r−(1−p)² ≈pr ²

where the last approximation holds for the case r≥1/p.

This discussion shows that an objection of the form

[R(s)] may not ensure functional safety without causing a varianceproblem. The baseline subtraction method for variance reduction may notoffer a sufficient remedy to the problem because the problem would shiftfrom a high variance of R(s) to an equally high variance of the baselineconstants whose estimation would equally suffer numeric instabilities.Moreover, if the probability of an accident is p, then on average atleast 1/p sequences should be sampled before obtaining an accidentevent. This implies a lower bound of 1/p samples of sequences for alearning algorithm that aims at minimizing

[R(s)]. The solution to this problem may be found in the architecturaldesign described herein, rather than through numerical conditioningtechniques. The approach here is based on the notion that hardconstraints should be injected outside of the learning framework. Inother words, the policy function may be decomposed into a learnable partand a nonlearnable part. Formally, the policy function may be structuredas π_(θ)=π^((T))∘π_(θ) ^((D)), where π_(θ) ^((D)) maps the (agnostic)state space into a set of Desires (e.g., desired navigational goals,etc.), while π^((T)) maps the Desires into a trajectory (which maydetermine how the car should move in a short range). The function π_(θ)^((D)) is responsible for the comfort of driving and for makingstrategic decisions such as which other cars should be over-taken orgiven way and what is the desired position of the host vehicle withinits lane, etc. The mapping from sensed navigational state to Desires isa policy π_(θ) ^((D)) that may be learned from experience by maximizingan expected reward. The desires produced by π_(θ) ^((D)) may betranslated into a cost function over driving trajectories. The functionπ^((T)), not a learned function, may be implemented by finding atrajectory that minimizes the cost subject to hard constraints onfunctional safety. This decomposition may ensure functional safety whileat the same time providing for comfortable driving.

A double merge navigational situation, as depicted in FIG. 11D, providesan example further illustrating these concepts. In a double merge,vehicles approach the merge area 1130 from both left and right sides.And, from each side, a vehicle, such as vehicle 1133 or vehicle 1135,can decide whether to merge into lanes on the other side of merge area1130. Successfully executing a double merge in busy traffic may requiresignificant negotiation skills and experience and may be difficult toexecute in a heuristic or brute force approach by enumerating allpossible trajectories that could be taken by all agents in the scene. Inthis double merge example, a set of Desires,

, appropriate for the double merge maneuver may be defined.

may be the Cartesian product of the following sets:

=[0,v_(max)]×L×{g,l,o}^(n), where [0, v_(max)] is the desired targetspeed of the host vehicle, L={1, 1.5, 2, 2.5, 3, 3.5, 4} is the desiredlateral position in lane units where whole numbers designate a lanecenter and fractional numbers designate lane boundaries, and {g, t, o}are classification labels assigned to each of the n other vehicles. Theother vehicles may be assigned “g” if the host vehicle is to give way tothe other vehicle, “t” if the host vehicle is to take way relative tothe other vehicle, or “o” if the host vehicle is to maintain an offsetdistance relative to the other vehicle.

Below is a description of how a set of Desires, (v,l,c₁, . . . c_(n))∈

, may be translated into a cost function over driving trajectories. Adriving trajectory may be represented by (x₁,y₁), . . . , (x_(k),y_(k)),where (x_(i),y_(i)) is the (lateral, longitudinal) location of the hostvehicle (in ego-centric units) at time r·i. In some experiments, r=0.1sec and k=10. Of course, other values may be selected as well. The costassigned to a trajectory may include a weighted sum of individual costsassigned to the desired speed, lateral position, and the label assignedto each of the other n vehicles.

Given a desired speed v∈[0, v_(max)], the cost of a trajectoryassociated with speed is

Σ_(i=2) ^(k)(v−∥(x _(i) ,y _(i))−(x _(i-1) ,y _(i-1))∥/Σ)².

Given desired lateral position, l∈L, the cost associated with desiredlateral position is

Σ_(i=1) ^(k)dist(x _(i) ,y _(i) ,l)

where dist(x, y, l) is the distance from the point (x, y) to the laneposition l. Regarding the cost due to other vehicles, for any othervehicle (x′₁, y′₁), . . . , (x′_(k), y′_(k)) may represent the othervehicle in egocentric units of the host vehicle, and i may be theearliest point for which there exists j such that the distance between(x_(i), y_(i)) and (x′_(j), y′_(j)) is small. If there is no such point,then i can be set as i=∞. If another car is classified as “give-way”, itmay be desirable that ri<rj+0.5, meaning that the host vehicle willarrive to the trajectory intersection point at least 0.5 seconds afterthe other vehicle will arrive at the same point. A possible formula fortranslating the above constraint into a cost is [r(j−i)+0.5]₊.

Likewise, if another car is classified as “take-way”, it may bedesirable that rj>ri+0.5, which may be translated to the cost[r(i−j)+0.5]₊. If another car is classified as “offset”, it may bedesirable that i=∞, meaning that the trajectory of the host vehicle andthe trajectory of the offset car do not intersect. This condition can betranslated to a cost by penalizing with respect to the distance betweentrajectories.

Assigning a weight to each of these costs may provide a single objectivefunction for the trajectory planner, π^((T)). A cost that encouragessmooth driving may be added to the objective. And, to ensure functionalsafety of the trajectory, hard constraints can be added to theobjective. For example, (x_(i), y_(i)) may be prohibited from being offthe roadway, and (x_(i), y_(i)) may be forbidden from being close to(x′_(j), y′_(j)) for any trajectory point (x′_(j), y′_(j)) of any othervehicle if |i−j| is small.

To summarize, the policy, π_(θ), can be decomposed into a mapping fromthe agnostic state to a set of Desires and a mapping from the Desires toan actual trajectory. The latter mapping is not based on learning andmay be implemented by solving an optimization problem whose cost dependson the Desires and whose hard constraints may guarantee functionalsafety of the policy.

The following discussion describes mapping from the agnostic state tothe set of Desires. As described above, to be compliant with functionalsafety, a system reliant upon reinforcement learning alone may suffer ahigh and unwieldy variance on the reward R(s). This result may beavoided by decomposing the problem into a mapping from (agnostic) statespace to a set of Desires using policy gradient iterations followed by amapping to an actual trajectory which does not involve a system trainedbased on machine learning.

For various reasons, the decision making may be further decomposed intosemantically meaningful components. For example, the size of

might be large and even continuous. In the double-merge scenariodescribed above with respect to FIG. 11D,

=[0, v_(max)]×L×{g, t, o}^(n)). Additionally, the gradient estimator mayinvolve the term Σ_(t=1) ^(T)∇_(θ)π_(θ)(a_(t)|s_(t)). In such anexpression, the variance may grow with the time horizon T. In somecases, the value of T may be roughly 250 which may be high enough tocreate significant variance. Supposing a sampling rate is in the rangeof 10 Hz and the merge area 1130 is 100 meters, preparation for themerge may begin approximately 300 meters before the merge area. If thehost vehicle travels at 16 meters per second (about 60 km per hour),then the value of T for an episode may be roughly 250.

Returning to the concept of an options graph, an options graph that maybe representative of the double merge scenario depicted in FIG. 11D isshown in FIG. 11E. As previously discussed, an options graph mayrepresent a hierarchical set of decisions organized as a DirectedAcyclic Graph (DAG). There may be a special node in the graph called the“root” node 1140, which may be the only node that has no incoming edges(e.g., decision lines). The decision process may traverse the graph,starting from the root node, until it reaches a “leaf” node, namely, anode that has no outgoing edges. Each internal node may implement apolicy function that chooses a child from among its available children.There may be a predefined mapping from the set of traversals over theoptions graph to the set of desires,

. In other words, a traversal on the options graph may be automaticallytranslated into a desire in

. Given a node, v, in the graph, a parameter vector θ_(v) may specifythe policy of choosing a child of v. If θ is the concatenation of allthe θ_(v), then π_(θ) ^((D)) may be defined by traversing from the rootof the graph to a leaf, while at each node v using the policy defined byθ_(v), to choose a child node.

In the double merge options graph 1139 of FIG. 11E, root node 1140 mayfirst decide if the host vehicle is within the merging area (e.g., area1130 of FIG. 11D) or if the host vehicle instead is approaching themerging area and needs to prepare for a possible merge. In both cases,the host vehicle may need to decide whether to change lanes (e.g., tothe left or to the right side) or whether to stay in the current lane.If the host vehicle has decided to change lanes, the host vehicle mayneed to decide whether conditions are suitable to go on and perform thelane change maneuver (e.g., at “go” node 1142). If it is not possible tochange lanes, the host vehicle may attempt to “push” toward the desiredlane (e.g., at node 1144 as part of a negotiation with vehicles in thedesired lane) by aiming at being on the lane mark. Alternatively, thehost vehicle may opt to “stay” in the same lane (e.g., at node 1146).Such a process may determine the lateral position for the host vehiclein a natural way. For example,

This may enable determination of the desired lateral position in anatural way. For example, if the host vehicle changes lanes from lane 2to lane 3, the “go” node may set the desired lateral position to 3, the“stay” node may set the desired lateral position to 2, and the “push”node may set the desired lateral position to 2.5. Next, the host vehiclemay decide whether to maintain the “same” speed (node 1148),“accelerate” (node 1150), or “decelerate” (node 1152). Next, the hostvehicle may enter a “chain like” structure 1154 that goes over the othervehicles and sets their semantic meaning to a value in the set {g, t,o}. This process may set the desires relative to the other vehicles. Theparameters of all nodes in this chain may be shared (similar toRecurrent Neural Networks).

A potential benefit of the options is the interpretability of theresults. Another potential benefit is that the decomposable structure ofthe set

can be relied upon and, therefore, the policy at each node may be chosenfrom among a small number of possibilities. Additionally, the structuremay allow for a reduction in the variance of the policy gradientestimator.

As discussed above, the length of an episode in the double mergescenario may be roughly T=250 steps. Such a value (or any other suitablevalue depending on a particular navigational scenario) may provideenough time to see the consequences of the host vehicle actions (e.g.,if the host vehicle decided to change lanes as a preparation for themerge, the host vehicle will see the benefit only after a successfulcompletion of the merge). On the other hand, due to the dynamic ofdriving, the host vehicle must make decisions at a fast enough frequency(e.g., 10 Hz in the case described above).

The options graph may enable a decrease in the effective value of Tin atleast two ways. First, given higher level decisions, a reward can bedefined for lower level decisions while taking into account shorterepisodes. For example, when the host vehicle has already chosen a “lanechange” and the “go” node, a policy can be learned for assigningsemantic meaning to vehicles by looking at episodes of 2-3 seconds(meaning that T becomes 20-30 instead of 250). Second, for high leveldecisions (such as whether to change lanes or to stay in the same lane),the host vehicle may not need to make decisions every 0.1 seconds.Instead, the host vehicle may be able to either make decisions at alower frequency (e.g., every second), or implement an “optiontermination” function, and then the gradient may be calculated onlyafter every termination of the option. In both cases, the effectivevalue of T may be an order of magnitude smaller than its original value.All in all, the estimator at every node may depend on a value of T whichis an order of magnitude smaller than the original 250 steps, which mayimmediately transfer to a smaller variance.

As discussed above, hard constraints may promote safer driving, andthere may be several different types of constraints. For example, statichard constraints may be defined directly from the sensing state. Thesemay include speed bumps, speed limits, road curvature, junctions, etc.,within the environment of the host vehicle that may implicate one ormore constraints on vehicle speed, heading, acceleration, breaking(deceleration), etc. Static hard constraints may also include semanticfree space where the host vehicle is prohibited from going outside ofthe free space and from navigating too close to physical barriers, forexample. Static hard constraints may also limit (e.g., prohibit)maneuvers that do not comply with various aspects of a kinematic motionof the vehicle, for example, a static hard constraint can be used toprohibit maneuvers that might lead to the host vehicle overturning,sliding, or otherwise losing control.

Hard constraints may also be associated with vehicles. For example, aconstraint may be employed requiring that a vehicle maintain alongitudinal distance to other vehicles of at least one meter and alateral distance from other vehicles of at least 0.5 meters. Constraintsmay also be applied such that the host vehicle will avoid maintaining acollision course with one or more other vehicles. For example, a time τmay be a measure of time based on a particular scene. The predictedtrajectories of the host vehicle and one or more other vehicles may beconsidered from a current time to time τ. Where the two trajectoriesintersect, (t_(i) ^(a), t_(i) ^(l)) may represent the time of arrivaland the leaving time of vehicle i to the intersection point. That is,each car will arrive at point when a first part of the car passes theintersection point, and a certain amount of time will be required beforethe last part of the car passes through the intersection point. Thisamount of time separates the arrival time from the leaving time.Assuming that t₁ ^(a)<t₂ ^(a) (i.e., that the arrival time of vehicle 1is less than the arrival time of vehicle 2), then we will want to ensurethat vehicle 1 has left the intersection point prior to vehicle 2arriving. Otherwise, a collision would result. Thus, a hard constraintmay be implemented such that t₁ ^(l)>t₂ ^(a). Moreover, to ensure thatvehicle 1 and vehicle 2 do not miss one another by a minimal amount, anadded margin of safety may be obtained by including a buffer time intothe constraint (e.g., 0.5 seconds or another suitable value). A hardconstraint relating to predicted intersection trajectories of twovehicles may be expressed as t₁ ^(l)>t₂ ^(a)+0.5.

The amount of time τ over which the trajectories of the host vehicle andone or more other vehicles are tracked may vary. In junction scenarios,however, where speeds may be lower, τ may be longer, and τ may bedefined such that a host vehicle will enter and leave the junction inless than τ seconds.

Applying hard constraints to vehicle trajectories, of course, requiresthat the trajectories of those vehicles be predicted. For the hostvehicle, trajectory prediction may be relatively straightforward, as thehost vehicle generally already understands and, indeed, is planning anintended trajectory at any given time. Relative to other vehicles,predicting their trajectories can be less straightforward. For othervehicles, the baseline calculation for determining predictedtrajectories may rely on the current speed and heading of the othervehicles, as determined, for example, based on analysis of an imagestream captured by one or more cameras and/or other sensors (radar,lidar, acoustic, etc.) aboard the host vehicle.

There can be some exceptions, however, that can simplify the problem orat least provide added confidence in a trajectory predicted for anothervehicle. For example, with respect to structured roads in which there isan indication of lanes and where give-way rules may exist, thetrajectories of other vehicles can be based, at least in part, upon theposition of the other vehicles relative to the lanes and based uponapplicable give-way rules. Thus, in some situations, when there areobserved lane structures, it may be assumed that next-lane vehicles willrespect lane boundaries. That is, the host vehicle may assume that anext-lane vehicle will stay in its lane unless there is observedevidence (e.g., a signal light, strong lateral movement, movement acrossa lane boundary) indicating that the next-lane vehicle will cut into thelane of the host vehicle.

Other situations may also provide clues regarding the expectedtrajectories of other vehicles. For example, at stop signs, trafficlights, roundabouts, etc., where the host vehicle may have the right ofway, it may be assumed that other vehicles will respect that right ofway. Thus, unless there is observed evidence of a rule break, othervehicles may be assumed to proceed along a trajectory that respects therights of way possessed by the host vehicle.

Hard constraints may also be applied with respect to pedestrians in anenvironment of the host vehicle. For example, a buffer distance may beestablished with respect to pedestrians such that the host vehicle isprohibited from navigating any closer than the prescribed bufferdistance relative to any observed pedestrian. The pedestrian bufferdistance may be any suitable distance. In some embodiments, the bufferdistance may be at least one meter relative to an observed pedestrian.

Similar to the situation with vehicles, hard constraints may also beapplied with respect to relative motion between pedestrians and the hostvehicle. For example, the trajectory of a pedestrian (based on a headingdirection and speed) may be monitored relative to the projectedtrajectory of the host vehicle. Given a particular pedestriantrajectory, with every point p on the trajectory, t(p) may represent thetime required for the pedestrian to reach point p. To maintain therequired buffer distance of at least 1 meter from the pedestrian, eithert(p) must be larger than the time the host vehicle will reach point p(with sufficient difference in time such that the host vehicle passes infront of the pedestrian by a distance of at least one meter) or thatt(p) must be less than the time the host vehicle will reach point p(e.g., if the host vehicle brakes to give way to the pedestrian). Still,in the latter example, the hard constraint may require that the hostvehicle arrive at point p at a sufficient time later than the pedestriansuch that the host vehicle can pass behind the pedestrian and maintainthe required buffer distance of at least one meter. Of course, there maybe exceptions to the pedestrian hard constraint. For example, where thehost vehicle has the right of way or where speeds are very slow, andthere is no observed evidence that the pedestrian will decline to giveway to the host vehicle or will otherwise navigate toward the hostvehicle, the pedestrian hard constraint may be relaxed (e.g., to asmaller buffer of at least 0.75 meters or 0.50 meters).

In some examples, constraints may be relaxed where it is determined thatnot all can be met. For example, in situations where a road is toonarrow to leave desired spacing (e.g., 0.5 meters) from both curbs orfrom a curb and a parked vehicle, one or more the constraints may berelaxed if there are mitigating circumstances. For example, if there areno pedestrians (or other objects) on the sidewalk one can proceed slowlyat 0.1 meters from a curb. In some embodiments, constraints may berelaxed if doing so will improve the user experience. For example, inorder to avoid a pothole, constraints may be relaxed to allow a vehicleto navigate closers to the edges of the lane, a curb, or a pedestrianmore than might ordinarily be permitted. Furthermore, when determiningwhich constrains to relax, in some embodiments, the one or moreconstraints chosen to relax are those deemed to have the least availablenegative impact to safety. For example, a constraint relating to howclose the vehicle may travel to the curb or to a concrete barrier may berelaxed before relaxing one dealing with proximity to other vehicles. Insome embodiments, pedestrian constraints may be the last to be relaxed,or may never be relaxed in some situations.

FIG. 12 shows an example of a scene that may be captured and analyzedduring navigation of a host vehicle. For example, a host vehicle mayinclude a navigation system (e.g., system 100), as described above, thatmay receive from a camera (e.g., at least one of image capture device122, image capture device 124, and image capture device 126) associatedwith the host vehicle a plurality of images representative of anenvironment of the host vehicle. The scene shown in FIG. 12 is anexample of one of the images that may be captured at time t from anenvironment of a host vehicle traveling in lane 1210 along a predictedtrajectory 1212. The navigation system may include at least oneprocessing device (e.g., including any of the EyeQ processors or otherdevices described above) that are specifically programmed to receive theplurality of images and analyze the images to determine an action inresponse to the scene.

Such programming may include, for example, access to instructions storedin a memory, access to instructions included in one or more of theavailable processors as part of a processor architecture/instructionset, etc., such that execution of such instructions contribute to thedisclosed operation of the system. Such programming may also include oneor more trained neural networks that can be relied upon by the one ormore processors to receive various navigational inputs and, based onprevious training, return various outputs useful in generating plannedtrajectories for a host vehicle.

In some embodiments, the at least one processing device may implementsensing module 801, driving policy module 803, and control module 805,as shown in FIG. 8. Sensing module 801 may be responsible for collectingand outputting the image information collected from the cameras andproviding that information, in the form of an identified navigationalstate, to driving policy module 803, which may constitute a trainednavigational system that has been trained through machine learningtechniques, such as supervised learning, reinforcement learning, etc.Based on the navigational state information provided to driving policymodule 803 by sensing module 801, driving policy module 803 (e.g., byimplementing the options graph approach described above) may generate adesired navigational action for execution by the host vehicle inresponse to the identified navigational state.

In some embodiments, the at least one processing device may translatethe desired navigation action directly into navigational commands using,for example, control module 805. In other embodiments, however, hardconstraints may be applied such that the desired navigational actionprovided by the driving policy module 803 is tested against variouspredetermined navigational constraints that may be implicated by thescene and the desired navigational action. For example, where drivingpolicy module 803 outputs a desired navigational action that would causethe host vehicle to follow trajectory 1212, this navigational action maybe tested relative to one or more hard constraints associated withvarious aspects of the environment of the host vehicle. For example, acaptured image 1201 may reveal a curb 1213, a pedestrian 1215, a targetvehicle 1217, and a stationary object (e.g., an overturned box) presentin the scene. Each of these may be associated with one or more hardconstraints. For example, curb 1213 may be associated with a staticconstraint that prohibits the host vehicle from navigating into the curbor past the curb and onto a sidewalk 1214. Curb 1213 may also beassociated with a road barrier envelope that defines a distance (e.g., abuffer zone) extending away from (e.g., by 0.1 meters, 0.25 meters, 0.5meters, 1 meter, etc.) and along the curb, which defines a no-navigatezone for the host vehicle. Of course, static constraints may beassociated with other types of roadside boundaries as well (e.g., guardrails, concrete pillars, traffic cones, pylons, or any other type ofroadside barrier).

It should be noted that distances and ranging may be determined by anysuitable method. For example, in some embodiments, distance informationmay be provided by onboard radar and/or lidar systems. Alternatively oradditionally, distance information may be derived from analysis of oneor more images captured from the environment of the host vehicle. Forexample, numbers of pixels of a recognized object represented in animage may be determined and compared to known field of view and focallength geometries of the image capture devices to determine scale anddistances. Velocities and accelerations may be determined, for example,by observing changes in scale between objects from image to image overknown time intervals. This analysis may indicate the direction ofmovement toward or away from the host vehicle along with how fast theobject is pulling away from or coming toward the host vehicle. Crossingvelocity may be determined through analysis of the change in an object'sX coordinate position from one image to another over known time periods.

Pedestrian 1215 may be associated with a pedestrian envelope thatdefines a buffer zone 1216. In some cases, an imposed hard constraintmay prohibit the host vehicle from navigating within a distance of 1meter from pedestrian 1215 (in any direction relative to thepedestrian). Pedestrian 1215 may also define the location of apedestrian influence zone 1220. Such an influence zone may be associatedwith a constraint that limits the speed of the host vehicle within theinfluence zone. The influence zone may extend 5 meters, 10 meters, 20meters, etc., from pedestrian 1215. Each graduation of the influencezone may be associated with a different speed limit. For example, withina zone of 1 meter to five meters from pedestrian 1215, host vehicle maybe limited to a first speed (e.g., 10 mph, 20 mph, etc.) that may beless than a speed limit in a pedestrian influence zone extending from 5meters to 10 meters. Any graduation for the various stages of theinfluence zone may be used. In some embodiments, the first stage may benarrower than from 1 meter to five meters and may extend only from onemeter to two meters. In other embodiments, the first stage of theinfluence zone may extend from 1 meter (the boundary of the no-navigatezone around a pedestrian) to a distance of at least 10 meters. A secondstage, in turn, may extend from 10 meters to at least about 20 meters.The second stage may be associated with a maximum rate of travel for thehost vehicle that is greater than the maximum rate of travel associatedwith the first stage of the pedestrian influence zone.

One or more stationary object constraints may also be implicated by thedetected scene in the environment of the host vehicle. For example, inimage 1201, the at least one processing device may detect a stationaryobject, such as box 1219 present in the roadway. Detected stationaryobjects may include various objects, such as at least one of a tree, apole, a road sign, or an object in a roadway. One or more predefinednavigational constraints may be associated with the detected stationaryobject. For example, such constraints may include a stationary objectenvelope, wherein the stationary object envelope defines a buffer zoneabout the object within which navigation of the host vehicle may beprohibited. At least a portion of the buffer zone may extend apredetermined distance from an edge of the detected stationary object.For example, in the scene represented by image 1201, a buffer zone of atleast 0.1 meters, 0.25 meters, 0.5 meters or more may be associated withbox 1219 such that the host vehicle will pass to the right or to theleft of the box by at least some distance (e.g., the buffer zonedistance) in order to avoid a collision with the detected stationaryobject.

The predefined hard constraints may also include one or more targetvehicle constraints. For example, a target vehicle 1217 may be detectedin image 1201. To ensure that the host vehicle does not collide withtarget vehicle 1217, one or more hard constraints may be employed. Insome cases, a target vehicle envelope may be associated with a singlebuffer zone distance. For example, the buffer zone may be defined by a 1meter distance surrounding the target vehicle in all directions. Thebuffer zone may define a region extending from the target vehicle by atleast one meter into which the host vehicle is prohibited fromnavigating.

The envelope surrounding target vehicle 1217 need not be defined by afixed buffer distance, however. In some cases the predefined hardconstraints associate with target vehicles (or any other movable objectsdetected in the environment of the host vehicle) may depend on theorientation of the host vehicle relative to the detected target vehicle.For example, in some cases, a longitudinal buffer zone distance (e.g.,one extending from the target vehicle toward the front or rear of thehost vehicle—such as in the case that the host vehicle is driving towardthe target vehicle) may be at least one meter. A lateral buffer zonedistance (e.g., one extending from the target vehicle toward either sideof the host vehicle—such as when the host vehicle is traveling in a sameor opposite direction as the target vehicle such that a side of the hostvehicle will pass adjacent to a side of the target vehicle) may be atleast 0.5 meters.

As described above, other constraints may also be implicated bydetection of a target vehicle or a pedestrian in the environment of thehost vehicle. For example, the predicted trajectories of the hostvehicle and target vehicle 1217 may be considered and where the twotrajectories intersect (e.g., at intersection point 1230), a hardconstraint may require t₁ ^(l)>t₂ ^(a) or t₁ ^(l)>t₂ ^(a)+0.5 where thehost vehicle is vehicle 1, and target vehicle 1217 is vehicle 2.Similarly, the trajectory of pedestrian 1215 (based on a headingdirection and speed) may be monitored relative to the projectedtrajectory of the host vehicle. Given a particular pedestriantrajectory, with every point p on the trajectory, t(p) will representthe time required for the pedestrian to reach point p (i.e., point 1231in FIG. 12). To maintain the required buffer distance of at least 1meter from the pedestrian, either t(p) must be larger than the time thehost vehicle will reach point p (with sufficient difference in time suchthat the host vehicle passes in front of the pedestrian by a distance ofat least one meter) or that t(p) must be less than the time the hostvehicle will reach point p (e.g., if the host vehicle brakes to give wayto the pedestrian). Still, in the latter example, the hard constraintwill require that the host vehicle arrive at point p at a sufficienttime later than the pedestrian such that the host vehicle can passbehind the pedestrian and maintain the required buffer distance of atleast one meter.

Other hard constraints may also be employed. For example, a maximumdeceleration rate of the host vehicle may be employed in at least somecases. Such a maximum deceleration rate may be determined based on adetected distance to a target vehicle following the host vehicle (e.g.,using images collected from a rearward facing camera). The hardconstraints may include a mandatory stop at a sensed crosswalk or arailroad crossing or other applicable constraints.

Where analysis of a scene in an environment of the host vehicleindicates that one or more predefined navigational constraints may beimplicated, those constraints may be imposed relative to one or moreplanned navigational actions for the host vehicle. For example, whereanalysis of a scene results in driving policy module 803 returning adesired navigational action, that desired navigational action may betested against one or more implicated constraints. If the desirednavigational action is determined to violate any aspect of theimplicated constraints (e.g., if the desired navigational action wouldcarry the host vehicle within a distance of 0.7 meters of pedestrian1215 where a predefined hard constraint requires that the host vehicleremain at least 1.0 meters from pedestrian 1215), then at least onemodification to the desired navigational action may be made based on theone or more predefined navigational constraints. Adjusting the desirednavigational action in this way may provide an actual navigationalaction for the host vehicle in compliance with the constraintsimplicated by a particular scene detected in the environment of the hostvehicle.

After determination of the actual navigational action for the hostvehicle, that navigational action may be implemented by causing at leastone adjustment of a navigational actuator of the host vehicle inresponse to the determined actual navigational action for the hostvehicle. Such navigational actuator may include at least one of asteering mechanism, a brake, or an accelerator of the host vehicle.

Prioritized Constraints

As described above, various hard constraints may be employed with anavigational system to ensure safe operation of a host vehicle. Theconstraints may include a minimum safe driving distance with respect toa pedestrian, a target vehicle, a road barrier, or a detected object, amaximum speed of travel when passing within an influence zone of adetected pedestrian, or a maximum deceleration rate for the hostvehicle, among others. These constraints may be imposed with a trainedsystem trained based on machine learning (supervised, reinforcement, ora combination), but they also may be useful with non-trained systems(e.g., those employing algorithms to directly address anticipatedsituations arising in scenes from a host vehicle environment).

In either case, there may be a hierarchy of constraints. In other words,some navigational constraints may have priority over other constraints.Thus, if a situation arose in which a navigational action was notavailable that would result in all implicated constraints beingsatisfied, the navigation system may determine the availablenavigational action that achieves the highest priority constraintsfirst. For example, the system may cause the vehicle to avoid apedestrian first even if navigation to avoid the pedestrian would resultin a collision with another vehicle or an object detected in a road. Inanother example, the system may cause the vehicle to ride up on a curbto avoid a pedestrian.

FIG. 13 provides a flowchart illustrating an algorithm for implementinga hierarchy of implicated constraints determined based on analysis of ascene in an environment of a host vehicle. For example, at step 1301, atleast one processing device associated with the navigational system(e.g., an EyeQ processor, etc.) may receive, from a camera mounted onthe host vehicle, a plurality of images representative of an environmentof the host vehicle. Through analysis of an image or imagesrepresentative of the scene of the host vehicle environment at step1303, a navigational state associated with the host vehicle may beidentified. For example, a navigational state may indicate that the hostvehicle is traveling along a two-lane road 1210, as in FIG. 12, that atarget vehicle 1217 is moving through an intersection ahead of the hostvehicle, that a pedestrian 1215 is waiting to cross the road on whichthe host vehicle travels, that an object 1219 is present ahead in thehost vehicle lane, among various other attributes of the scene.

At step 1305, one or more navigational constraints implicated by thenavigational state of the host vehicle may be determined. For example,the at least one processing device, after analyzing a scene in theenvironment of the host vehicle represented by one or more capturedimages may determine one or more navigational constraints implicated byobjects, vehicles, pedestrians, etc., recognized through image analysisof the captured images. In some embodiments, the at least one processingdevice may determine at least a first predefined navigational constraintand a second predefined navigational constraint implicated by thenavigational state, and the first predefined navigational constraint maydiffer from the second predefined navigational constraint. For example,the first navigational constraint may relate to one or more targetvehicles detected in the environment of the host vehicle, and the secondnavigational constraint may relate to a pedestrian detected in theenvironment of the host vehicle.

At step 1307, the at least one processing device may determine apriority associated with constraints identified in step 1305. In theexample described, the second predefined navigational constraint,relating to pedestrians, may have a priority higher than the firstpredefined navigational constraint, which relates to target vehicles.While priorities associated with navigational constraints may bedetermined or assigned based on various factors, in some embodiments,the priority of a navigational constraint may be related to its relativeimportance from a safety perspective. For example, while it may beimportant that all implemented navigational constraints be followed orsatisfied in as many situations as possible, some constraints may beassociated with greater safety risks than others and, therefore, may beassigned higher priorities. For example, a navigational constraintrequiring that the host vehicle maintain at least a 1 meter spacing froma pedestrian may have a higher priority than a constraint requiring thatthe host vehicle maintain at least a 1 meter spacing from a targetvehicle. This may be because a collision with a pedestrian may have moresevere consequences than a collision with another vehicle. Similarly,maintaining a space between the host vehicle and a target vehicle mayhave a higher priority than a constraint requiring the host vehicle toavoid a box in the road, to drive less than a certain speed over a speedbump, or to expose the host vehicle occupants to no more than a maximumacceleration level.

While driving policy module 803 is designed to maximize safety bysatisfying navigational constraints implicated by a particular scene ornavigational state, in some situations it may be physically impossibleto satisfy every implicated constraint. In such situations, the priorityof each implicated constraint may be used to determine which of theimplicated constraints should be satisfied first, as shown at step 1309.Continuing with the example above, in a situation where it is notpossible satisfy both the pedestrian gap constraint and the targetvehicle gap constraint, but rather only one of the constraints can besatisfied, then the higher priority of the pedestrian gap constraint mayresult in that constraint being satisfied before attempting to maintaina gap to the target vehicle. Thus, in normal situations, the at leastone processing device may determine, based on the identifiednavigational state of the host vehicle, a first navigational action forthe host vehicle satisfying both the first predefined navigationalconstraint and the second predefined navigational constraint where boththe first predefined navigational constraint and the second predefinednavigational constraint can be satisfied, as shown at step 1311. Inother situations, however, where not all the implicated constraints canbe satisfied, the at least one processing device may determine, based onthe identified navigational state, a second navigational action for thehost vehicle satisfying the second predefined navigational constraint(i.e., the higher priority constraint), but not satisfying the firstpredefined navigational constraint (having a priority lower than thesecond navigational constraint), where the first predefined navigationalconstraint and the second predefined navigational constraint cannot bothbe satisfied, as shown at step 1313.

Next, at step 1315, to implement the determined navigational actions forthe host vehicle the at least one processing device can cause at leastone adjustment of a navigational actuator of the host vehicle inresponse to the determined first navigational action or the determinedsecond navigational action for the host vehicle. As in previous example,the navigational actuator may include at least one of a steeringmechanism, a brake, or an accelerator.

Constraint Relaxation

As discussed above, navigational constraints may be imposed for safetypurposes. The constraints may include a minimum safe driving distancewith respect to a pedestrian, a target vehicle, a road barrier, or adetected object, a maximum speed of travel when passing within aninfluence zone of a detected pedestrian, or a maximum deceleration ratefor the host vehicle, among others. These constraints may be imposed ina learning or non-learning navigational system. In certain situations,these constraints may be relaxed. For example, where the host vehicleslows or stops near a pedestrian, then progresses slowly to convey anintention to pass by the pedestrian, a response of the pedestrian can bedetected from acquired images. If the response of the pedestrian is tostay still or to stop moving (and/or if eye contact with the pedestrianis sensed), it may be understood that the pedestrian recognizes anintent of the navigational system to pass by the pedestrian. In suchsituations, the system may relax one or more predefined constraints andimplement a less stringent constraint (e.g., allow the vehicle tonavigate within 0.5 meters of a pedestrian rather than within a morestringent 1 meter boundary).

FIG. 14 provides a flowchart for implementing control of the hostvehicle based on relaxation of one or more navigational constraints. Atstep 1401, the at least one processing device may receive, from a cameraassociated with the host vehicle, a plurality of images representativeof an environment of the host vehicle. Analysis of the images at step1403 may enable identification of a navigational state associated withthe host vehicle. At step 1405, the at least one processor may determinenavigational constraints associated with the navigational state of thehost vehicle. The navigational constraints may include a firstpredefined navigational constraint implicated by at least one aspect ofthe navigational state. At step 1407, analysis of the plurality ofimages may reveal the presence of at least one navigational constraintrelaxation factor.

A navigational constraint relaxation factor may include any suitableindicator that one or more navigational constraints may be suspended,altered, or otherwise relaxed in at least one aspect. In someembodiments, the at least one navigational constraint relaxation factormay include a determination (based on image analysis) that the eyes of apedestrian are looking in a direction of the host vehicle. In suchcases, it may more safely be assumed that the pedestrian is aware of thehost vehicle. As a result, a confidence level may be higher that thepedestrian will not engage in unexpected actions that cause thepedestrian to move into a path of the host vehicle. Other constraintrelaxation factors may also be used. For example, the at least onenavigational constraint relaxation factor may include: a pedestriandetermined to be not moving (e.g., one presumed to be less likely ofentering a path of the host vehicle); or a pedestrian whose motion isdetermined to be slowing. The navigational constraint relaxation factormay also include more complicated actions, such as a pedestriandetermined to be not moving after the host vehicle has come to a stopand then resumed movement. In such a situation, the pedestrian may beassumed to understand that the host vehicle has a right of way, and thepedestrian coming to a stop may suggest an intent of the pedestrian togive way to the host vehicle. Other situations that may cause one ormore constraints to be relaxed include the type of curb stone (e.g., alow curb stone or one with a gradual slope might allow a relaxeddistance constraint), lack of pedestrians or other objects on sidewalk,a vehicle with its engine not running may have a relaxed distance, or aa situation in which a pedestrian is facing away and/or is moving awayfrom the area towards which the host vehicle is heading.

Where the presence of a navigational constraint relaxation factor isidentified (e.g., at step 1407), a second navigational constraint may bedetermined or developed in response to detection of the constraintrelaxation factor. This second navigational constraint may be differentfrom the first navigational constraint and may include at least onecharacteristic relaxed with respect to the first navigationalconstraint. The second navigational constraint may include a newlygenerated constraint based on the first constraint, where the newlygenerated constraint includes at least one modification that relaxes thefirst constraint in at least one respect. Alternatively, the secondconstraint may constitute a predetermined constraint that is lessstringent than the first navigational constraint in at least onerespect. In some embodiments, such second constraints may be reservedfor usage only for situations where a constraint relaxation factor isidentified in an environment of the host vehicle. Whether the secondconstraint is newly generated or selected from a set of fully orpartially available predetermined constraints, application of a secondnavigational constraint in place of a more stringent first navigationalconstraint (that may be applied in the absence of detection of relevantnavigational constraint relaxation factors) may be referred to asconstraint relaxation and may be accomplished in step 1409.

Where at least one constraint relaxation factor is detected at step1407, and at least one constraint has been relaxed in step 1409, anavigational action for the host vehicle may be determined at step 1411.The navigational action for the host vehicle may be based on theidentified navigational state and may satisfy the second navigationalconstraint. The navigational action may be implemented at step 1413 bycausing at least one adjustment of a navigational actuator of the hostvehicle in response to the determined navigational action.

As discussed above, the usage of navigational constraints and relaxednavigational constraints may be employed with navigational systems thatare trained (e.g., through machine learning) or untrained (e.g., systemsprogrammed to respond with predetermined actions in response to specificnavigational states). Where trained navigational systems are used, theavailability of relaxed navigational constraints for certainnavigational situations may represent a mode switching from a trainedsystem response to an untrained system response. For example, a trainednavigational network may determine an original navigational action forthe host vehicle, based on the first navigational constraint. The actiontaken by the vehicle, however, may be one that is different from thenavigational action satisfying the first navigational constraint.Rather, the action taken may satisfy the more relaxed secondnavigational constraint and may be an action developed by a non-trainedsystem (e.g., as a response to detection of a particular condition inthe environment of the host vehicle, such as the presence of anavigational constraint relaxation factor).

There are many examples of navigational constraints that may be relaxedin response to detection in the environment of the host vehicle of aconstraint relaxation factor. For example, where a predefinednavigational constraint includes a buffer zone associated with adetected pedestrian, and at least a portion of the buffer zone extends adistance from the detected pedestrian, a relaxed navigational constraint(either newly developed, called up from memory from a predetermined set,or generated as a relaxed version of a preexisting constraint) mayinclude a different or modified buffer zone. For example, the differentor modified buffer zone may have a distance relative to the pedestrianthat is less than the original or unmodified buffer zone relative to thedetected pedestrian. As a result, in view of the relaxed constraint, thehost vehicle may be permitted to navigate closer to a detectedpedestrian, where an appropriate constraint relaxation factor isdetected in the environment of the host vehicle.

A relaxed characteristic of a navigational constraint may include areduced width in a buffer zone associated with at least one pedestrian,as noted above. The relaxed characteristic, however, may also include areduced width in a buffer zone associated with a target vehicle, adetected object, a roadside barrier, or any other object detected in theenvironment of the host vehicle.

The at least one relaxed characteristic may also include other types ofmodifications in navigational constraint characteristics. For example,the relaxed characteristic may include an increase in speed associatedwith at least one predefined navigational constraint. The relaxedcharacteristic may also include an increase in a maximum allowabledeceleration/acceleration associated with at least one predefinednavigational constraint.

While constraints may be relaxed in certain situations, as describedabove, in other situations, navigational constraints may be augmented.For example, in some situations, a navigational system may determinethat conditions warrant augmentation of a normal set of navigationalconstraints. Such augmentation may include adding new constraints to apredefined set of constraints or adjusting one or more aspects of apredefined constraint. The addition or adjustment may result in moreconservative navigation relative the predefined set of constraintsapplicable under normal driving conditions. Conditions that may warrantconstraint augmentation may include sensor failure, adverseenvironmental conditions (rain, snow, fog, or other conditionsassociated with reduced visibility or reduced vehicle traction), etc.

FIG. 15 provides a flowchart for implementing control of the hostvehicle based on augmentation of one or more navigational constraints.At step 1501, the at least one processing device may receive, from acamera associated with the host vehicle, a plurality of imagesrepresentative of an environment of the host vehicle. Analysis of theimages at step 1503 may enable identification of a navigational stateassociated with the host vehicle. At step 1505, the at least oneprocessor may determine navigational constraints associated with thenavigational state of the host vehicle. The navigational constraints mayinclude a first predefined navigational constraint implicated by atleast one aspect of the navigational state. At step 1507, analysis ofthe plurality of images may reveal the presence of at least onenavigational constraint augmentation factor.

An implicated navigational constraint may include any of thenavigational constraints discussed above (e.g., with respect to FIG. 12)or any other suitable navigational constraints. A navigationalconstraint augmentation factor may include any indicator that one ormore navigational constraints may be supplemented/augmented in at leastone aspect. Supplementation or augmentation of navigational constraintsmay be performed on a per set basis (e.g., by adding new navigationalconstraints to a predetermined set of constraints) or may be performedon a per constraint basis (e.g., modifying a particular constraint suchthat the modified constraint is more restrictive than the original, oradding a new constraint that corresponds to a predetermined constraint,wherein the new constraint is more restrictive than the correspondingconstraint in at least one aspect). Additionally, or alternatively,supplementation or augmentation of navigational constraints may referselection from among a set of predetermined constraints based on ahierarchy. For example, a set of augmented constraints may be availablefor selection based on whether a navigational augmentation factor isdetected in the environment of or relative to the host vehicle. Undernormal conditions where no augmentation factor is detected, then theimplicated navigational constraints may be drawn from constraintsapplicable to normal conditions. On the other hand, where one or moreconstraint augmentation factors are detected, the implicated constraintsmay be drawn from augmented constraints either generated or predefinedrelative to the one or more augmentation factors. The augmentedconstraints may be more restrictive in at least one aspect thancorresponding constraints applicable under normal conditions.

In some embodiments, the at least one navigational constraintaugmentation factor may include a detection (e.g., based on imageanalysis) of the presence of ice, snow, or water on a surface of a roadin the environment of the host vehicle. Such a determination may bebased, for example, upon detection of: areas of reflectance higher thanexpected for dry roadways (e.g., indicative of ice or water on theroadway); white regions on the road indicating the presence of snow;shadows on the roadway consistent with the presence of longitudinaltrenches (e.g., tire tracks in snow) on the roadway; water droplets orice/snow particles on a windshield of the host vehicle; or any othersuitable indicator of the presence of water or ice/snow on a surface ofa road.

The at least one navigational constraint augmentation factor may alsoinclude detection of particulates on an outer surface of a windshield ofthe host vehicle. Such particulates may impair image quality of one ormore image capture devices associated with the host vehicle. Whiledescribed with respect to a windshield of the host vehicle, which isrelevant for cameras mounted behind the windshield of the host vehicle,detection of particulates on other surfaces (e.g., a lens or lens coverof a camera, headlight lens, rear windshield, a tail light lens, or anyother surface of the host vehicle visible to an image capture device (ordetected by a sensor) associated with the host vehicle may also indicatethe presence of a navigational constraint augmentation factor.

The navigational constraint augmentation factor may also be detected asan attribute of one or more image acquisition devices. For example, adetected decrease in image quality of one or more images captured by animage capture device (e.g., a camera) associated with the host vehiclemay also constitute a navigational constraint augmentation factor. Adecline in image quality may be associated with a hardware failure orpartial hardware failure associated with the image capture device or anassembly associated with the image capture device. Such a decline inimage quality may also be caused by environmental conditions. Forexample, the presence of smoke, fog, rain, snow, etc., in the airsurrounding the host vehicle may also contribute to reduced imagequality relative to the road, pedestrians, target vehicles, etc., thatmay be present in an environment of the host vehicle.

The navigational constraint augmentation factor may also relate to otheraspects of the host vehicle. For example, in some situations, thenavigational constraint augmentation factor may include a detectedfailure or partial failure of a system or sensor associate with the hostvehicle. Such an augmentation factor may include, for example, detectionof failure or partial failure of a speed sensor, GPS receiver,accelerometer, camera, radar, lidar, brakes, tires, or any other systemassociated with the host vehicle that may impact the ability of the hostvehicle to navigate relative to navigational constraints associated witha navigational state of the host vehicle.

Where the presence of a navigational constraint augmentation factor isidentified (e.g., at step 1507), a second navigational constraint may bedetermined or developed in response to detection of the constraintaugmentation factor. This second navigational constraint may bedifferent from the first navigational constraint and may include atleast one characteristic augmented with respect to the firstnavigational constraint. The second navigational constraint may be morerestrictive than the first navigational constraint, because detection ofa constraint augmentation factor in the environment of the host vehicleor associated with the host vehicle may suggest that the host vehiclemay have at least one navigational capability reduced with respect tonormal operating conditions. Such reduced capabilities may includelowered road traction (e.g., ice, snow, or water on a roadway; reducedtire pressure; etc.); impaired vision (e.g., rain, snow, dust, smoke,fog etc. that reduces captured image quality); impaired detectioncapability (e.g., sensor failure or partial failure, reduced sensorperformance, etc.), or any other reduction in capability of the hostvehicle to navigate in response to a detected navigational state.

Where at least one constraint augmentation factor is detected at step1507, and at least one constraint has been augmented in step 1509, anavigational action for the host vehicle may be determined at step 1511.The navigational action for the host vehicle may be based on theidentified navigational state and may satisfy the second navigational(i.e., augmented) constraint. The navigational action may be implementedat step 1513 by causing at least one adjustment of a navigationalactuator of the host vehicle in response to the determined navigationalaction.

As discussed, the usage of navigational constraints and augmentednavigational constraints may be employed with navigational systems thatare trained (e.g., through machine learning) or untrained (e.g., systemsprogrammed to respond with predetermined actions in response to specificnavigational states). Where trained navigational systems are used, theavailability of augmented navigational constraints for certainnavigational situations may represent a mode switching from a trainedsystem response to an untrained system response. For example, a trainednavigational network may determine an original navigational action forthe host vehicle, based on the first navigational constraint. The actiontaken by the vehicle, however, may be one that is different from thenavigational action satisfying the first navigational constraint.Rather, the action taken may satisfy the augmented second navigationalconstraint and may be an action developed by a non-trained system (e.g.,as a response to detection of a particular condition in the environmentof the host vehicle, such as the presence of a navigational constraintaugmented factor).

There are many examples of navigational constraints that may begenerated, supplemented, or augmented in response to detection in theenvironment of the host vehicle of a constraint augmentation factor. Forexample, where a predefined navigational constraint includes a bufferzone associated with a detected pedestrian, object, vehicle, etc., andat least a portion of the buffer zone extends a distance from thedetected pedestrian/object/vehicle, an augmented navigational constraint(either newly developed, called up from memory from a predetermined set,or generated as an augmented version of a preexisting constraint) mayinclude a different or modified buffer zone. For example, the differentor modified buffer zone may have a distance relative to thepedestrian/object/vehicle that is greater than the original orunmodified buffer zone relative to the detectedpedestrian/object/vehicle. As a result, in view of the augmentedconstraint, the host vehicle may be forced to navigate further from thedetected pedestrian/object/vehicle, where an appropriate constraintaugmentation factor is detected in the environment of the host vehicleor relative to the host vehicle.

The at least one augmented characteristic may also include other typesof modifications in navigational constraint characteristics. Forexample, the augmented characteristic may include a decrease in speedassociated with at least one predefined navigational constraint. Theaugmented characteristic may also include a decrease in a maximumallowable deceleration/acceleration associated with at least onepredefined navigational constraint.

Navigation Based on Long Range Planning

In some embodiments, the disclosed navigational system can respond notonly to a detected navigational state in an environment of the hostvehicle, but may also determine one or more navigational actions basedon long range planning. For example, the system may consider thepotential impact on future navigational states of one or morenavigational actions available as options for navigating with respect toa detected navigational state. Considering the effects of availableactions on future states may enable the navigational system to determinenavigational actions based not just upon a currently detectednavigational state, but also based upon long range planning. Navigationusing long range planning techniques may be especially applicable whereone or more reward functions are employed by the navigation system as atechnique for selecting navigational actions from among availableoptions. Potential rewards may be analyzed with respect to the availablenavigational actions that may be taken in response to a detected,current navigational state of the host vehicle. Further, however, thepotential rewards may also be analyzed relative to actions that may betaken in response to future navigational states projected to result fromthe available actions to a current navigational state. As a result, thedisclosed navigational system may, in some cases, select a navigationalaction in response to a detected navigational state even where theselected navigational action may not yield the highest reward from amongthe available actions that may be taken in response to the currentnavigational state. This may be especially true where the systemdetermines that the selected action may result in a future navigationalstate giving rise to one or more potential navigational actions offeringhigher rewards than the selected action or, in some cases, any of theactions available relative to a current navigational state. Theprinciple may be expressed more simply as taking a less favorable actionnow in order to produce higher reward options in the future. Thus, thedisclosed navigational system capable of long range planning may choosea suboptimal short term action where long term prediction indicates thata short term loss in reward may result in long term reward gains.

In general, autonomous driving applications may involve a series ofplanning problems, where the navigational system may decide on immediateactions in order to optimize a longer term objective. For example, whena vehicle is confronted with a merge situation at a roundabout, thenavigational system may decide on an immediate acceleration or brakingcommand in order to initiate navigation into the roundabout. While theimmediate action to the detected navigational state at the roundaboutmay involve an acceleration or braking command responsive to thedetected state, the long term objective is a successful merge, and thelong term effect of the selected command is the success/failure of themerge. The planning problem may be addressed by decomposing the probleminto two phases. First, supervised learning may be applied forpredicting the near future based on the present (assuming the predictorwill be differentiable with respect to the representation of thepresent). Second, a full trajectory of the agent may be modeled using arecurrent neural network, where unexplained factors are modeled as(additive) input nodes. This may allow solutions to the long-termplanning problem to be determined using supervised learning techniquesand direct optimization over the recurrent neural network. Such anapproach may also enable the learning of robust policies byincorporating adversarial elements to the environment.

Two of the most fundamental elements of autonomous driving systems aresensing and planning Sensing deals with finding a compact representationof the present state of the environment, while planning deals withdeciding on what actions to take so as to optimize future objectives.Supervised machine learning techniques are useful for solving sensingproblems. Machine learning algorithmic frameworks may also be used forthe planning part, especially reinforcement learning (RL) frameworks,such as those described above.

RL may be performed in a sequence of consecutive rounds. At round t, theplanner (a.k.a. the agent or driving policy module 803) may observe astate, s_(t)∈S, which represents the agent as well as the environment.It then should decide on an action a_(t)∈A. After performing the action,the agent receives an immediate reward, r_(t)∈

, and is moved to a new state, s_(t+1). As an example, the host vehiclemay include an adaptive cruise control (ACC) system, in which thevehicle should autonomously implement acceleration/braking so as to keepan adequate distance to a preceding vehicle while maintaining smoothdriving. The state can be modeled as a pair, s_(t)=(x_(t), v_(t))∈

₂, where x_(t) is the distance to the preceding vehicle and v_(t) is thevelocity of the host vehicle relative to the velocity of the precedingvehicle. The action a_(t)∈

will be the acceleration command (where the host vehicle slows down ifa_(t)<0). The reward can be a function that depends on |a_(t)|(reflecting the smoothness of driving) and on s_(t) (reflecting that thehost vehicle maintains a safe distance from the preceding vehicle). Thegoal of the planner is to maximize the cumulative reward (maybe up to atime horizon or a discounted sum of future rewards). To do so, theplanner may rely on a policy, π: S→A, which maps a state into an action.

Supervised Learning (SL) can be viewed as a special case of RL, in whichs_(t) is sampled from some distribution over S, and the reward functionmay have the form r_(t)∈−

(a_(t), y_(t)), where

is a loss function, and the learner observes the value of y_(t) which isthe (possibly noisy) value of the optimal action to take when viewingthe state s_(t). There may be several differences between a general RLmodel and a specific case of SL, and these differences can make thegeneral RL problem more challenging.

In some SL situations, the actions (or predictions) taken by the learnermay have no effect on the environment. In other words, s_(t+1) and a_(t)are independent. This can have two important implications. First, in SL,a sample (s₁, y₁), . . . , (s_(m), y_(m)) can be collected in advance,and only then can the search begin for a policy (or predictor) that willhave good accuracy relative to the sample. In contrast, in RL, the states_(t+1) usually depends on the action taken (and also on the previousstate), which in turn depends on the policy used to generate the action.This ties the data generation process to the policy learning process.Second, because actions do not affect the environment in SL, thecontribution of the choice of a_(t) to the performance of π is local.Specifically, a_(t) only affects the value of the immediate reward. Incontrast, in RL, actions that are taken at round t might have along-term effect on the reward values in future rounds.

In SL, the knowledge of the “correct” answer, y_(t), together with theshape of the reward, r_(t)=−

(a_(t), y_(t)) may provide full knowledge of the reward for all possiblechoices of a_(t), which may enable calculation of the derivative of thereward with respect to a_(t). In contrast, in RL, a “one-shot” value ofthe reward may be all that can be observed for a specific choice ofaction taken. This may be referred to as a “bandit” feedback. This isone of the most significant reasons for the need of “exploration” as apart of long term navigational planning, because in RL-based systems, ifonly “bandit” feedback is available, the system may not always know ifthe action taken was the best action to take.

Many RL algorithms rely, at least in part, on the mathematically elegantmodel of a Markov Decision Process (MDP). The Markovian assumption isthat the distribution of s_(t+1) is fully determined given s_(t) anda_(t). This yields a closed form expression for the cumulative reward ofa given policy in terms of the stationary distribution over states ofthe MDP. The stationary distribution of a policy can be expressed as asolution to a linear programming problem. This yields two families ofalgorithms: 1) optimization with respect to the primal problem, whichmay be referred to as policy search, and 2) optimization with respect toa dual problem, whose variables are called the value function, V^(π).The value function determines the expected cumulative reward if the MDPbegins from the initial state, s, and from there actions are chosenaccording to π. A related quantity is the state-action value function,Q^(π)(s, a), which determines the cumulative reward assuming a startfrom state, s, an immediately chosen action a, and from there on actionschosen according to π. The Q function may give rise to acharacterization of the optimal policy (using the Bellman's equation).In particular, the Q function may show that the optimal policy is adeterministic function from S to A (in fact, it may be characterized asa “greedy” policy with respect to the optimal Q function).

One potential advantage of the MDP model is that it allows coupling ofthe future into the present using the Q function. For example, giventhat a host vehicle is now in state, s, the value of Q^(π)(s, a) mayindicate the effect of performing action a on the future. Therefore, theQ function may provide a local measure of the quality of an action a,thus making the RL problem more similar to a SL scenario.

Many RL algorithms approximate the V function or the Q function in oneway or another. Value iteration algorithms, e.g., the Q learningalgorithm, may rely on the fact that the V and Q functions of theoptimal policy may be fixed points of some operators derived fromBellman's equation. Actor-critic policy iteration algorithms aim tolearn a policy in an iterative way, where at iteration t, the “critic”estimates Q^(π) ^(t) and based on this estimate, the “actor” improvesthe policy.

Despite the mathematical elegancy of MDPs and the conveniency ofswitching to the Q function representation, this approach may haveseveral limitations. For example, an approximate notion of a Markovianbehaving state may be all that can be found in some cases. Furthermore,the transition of states may depend not only on the agent's action, butalso on actions of other players in the environment. For example, in theACC example mentioned above, while the dynamic of the autonomous vehiclemay be Markovian, the next state may depend on the behavior of thedriver of the other car, which is not necessarily Markovian. Onepossible solution to this problem is to use partially observed MDPs, inwhich it is assumed that there is a Markovian state, but an observationthat is distributed according to the hidden state is what can be seen.

A more direct approach may consider game theoretical generalizations ofMDPs (e.g., the Stochastic Games framework). Indeed, algorithms for MDPsmay be generalized to multi-agents games (e.g., minimax-Q learning orNash-Q learning). Other approaches may include explicit modeling of theother players and vanishing regret learning algorithms Learning in amulti-agent setting may be more complex than in a single agent setting.

A second limitation of the Q function representation may arise bydeparting from a tabular setting. The tabular setting is when the numberof states and actions is small, and therefore, Q can be expressed as atable with |S| rows and |A| columns. However, if the naturalrepresentation of S and A includes Euclidean spaces, and the state andaction spaces are discretized, the number of states/actions may beexponential in the dimension. In such cases, it may not be practical toemploy a tabular setting. Instead, the Q function may be approximated bysome function from a parametric hypothesis class (e.g., neural networksof a certain architecture). For example, a deep-Q-network (DQN) learningalgorithm may be used. In DQN, the state space can be continuous, butthe action space may remain a small discrete set. There may beapproaches for dealing with continuous action spaces, but they may relyon approximating the Q function. In any case, the Q function may becomplicated and sensitive to noise, and, therefore, may be challengingto learn.

A different approach may be to address the RL problem using a recurrentneural network (RNN). In some cases, RNN may be combined with thenotions of multi-agents games and robustness to adversarial environmentsfrom game theory. Further, this approach may be one that does notexplicitly rely on any Markovian assumption.

The following describes in more detail an approach for navigation byplanning based on prediction. In this approach, it may be assumed thatthe state space, S, is a subset of

^(d), and the action space, A, is a subset of

^(k). This may be a natural representation in many applications. Asnoted above, there may be two key differences between RL and SL: (1)because past actions affect future rewards, information from the futuremay need to be propagated back to the past; and (2) the “bandit” natureof rewards can blur the dependence between (state, action) and reward,which can complicate the learning process.

As a first step in the approach, an observation may be made that thereare interesting problems in which the bandit nature of rewards is not anissue. For example, reward value (as will be discussed in more detailbelow) for the ACC application may be differentiable with respect to thecurrent state and action. In fact, even if the reward is given in a“bandit” manner, the problem of learning a differentiable function,{circumflex over (r)}(s, a), such that {circumflex over (r)}(s_(t),a_(t))≈r_(t), may be a relatively straightforward SL problem (e.g., aone dimensional regression problem). Therefore, the first step of theapproach may be to either define the reward as a function, {circumflexover (r)}(s, a), which is differentiable with respect to s and a, or touse a regression learning algorithm in order to learn a differentiablefunction, {circumflex over (r)}, that minimizes at least some regressionloss over a sample with instance vector being (s_(t), a_(t))∈

^(d)×

^(k) and target scalar being r_(t). In some situations, in order tocreate a training set, elements of exploration may be used.

To address the connection between past and future, a similar idea may beused. For example, suppose a differentiable function {circumflex over(N)}(s, a) can be learned such that {circumflex over (N)}(s_(t),a_(t))≈s_(t+1). Learning such a function may be characterized as an SLproblem. {circumflex over (N)} may be viewed as a predictor for the nearfuture. Next, a policy that maps from S to A may be described using aparametric function π_(θ): S→A. Expressing π_(θ) as a neural network,may enable expression of an episode of running the agent for T roundsusing a recurrent neural network (RNN), where the next state is definedas s_(t+1)={circumflex over (N)}(s_(t),a_(t))+v_(t). Here, v_(t)∈

^(d) may be defined by the environment and may express unpredictableaspects of the near future. The fact that s_(t+1) depends on s_(t) anda_(t) in a differentiable manner may enable a connection between futurereward values and past actions. A parameter vector of the policyfunction, π_(θ), may be learned by back-propagation over the resultingRNN. Note that explicit probabilistic assumptions need not be imposed onv_(t). In particular, there need not be a requirement for a Markovianrelation. Instead, the recurrent network may be relied upon to propagate“enough” information between past and future. Intuitively, {circumflexover (N)}(s_(t),a_(t)) may describe the predictable part of the nearfuture, while v_(t) may express the unpredictable aspects, which mayarise due to the behavior of other players in the environment. Thelearning system should learn a policy that will be robust to thebehavior of other players. If ∥v_(i)∥ is large, the connection betweenpast actions and future reward values may be too noisy for learning ameaningful policy. Explicitly expressing the dynamic of the system in atransparent way may enable incorporation of prior knowledge more easily.For example, prior knowledge may simplify the problem of defining{circumflex over (N)}.

As discussed above, the learning system may benefit from robustnessrelative to an adversarial environment, such as the environment of ahost vehicle, which may include multiple other drivers that may act inunexpected way. In a model that does not impose probabilisticassumptions on v_(t), environments may be considered in which v_(t) ischosen in an adversarial manner. In some cases, restrictions may beplaced on μ_(t), otherwise the adversary can make the planning problemdifficult or even impossible. One natural restriction may be to requirethat ∥μ_(t)∥ is bounded by a constant.

Robustness against adversarial environments may be useful in autonomousdriving applications. Choosing μ_(t) in an adversarial way may evenspeed up the learning process, as it can focus the learning systemtoward a robust optimal policy. A simple game may be used to illustratethis concept. The state is s_(t)∈

, the action is a_(t)∈

, and the immediate loss function is 0.1|a_(t)|+[|s_(t)|−2]₊, where[x]₊=max{x, 0} is the ReLU (rectified linear unit) function. The nextstate is s_(t+1)=s_(t)+a_(t)+v_(t), where v_(t)∈[−0.5, 0.5] is chosenfor the environment in an adversarial manner. Here, the optimal policymay be written as a two layer network with ReLU:a_(t)=−[s_(t)−1.5]₊+[−s_(t)−1.5]₊. Observe that when |s_(t)|∈(1.5, 2],the optimal action may have a larger immediate loss than the action a=0.Therefore, the system may plan for the future and may not rely solely onthe immediate loss. Observe that the derivative of the loss with respectto a_(t) is 0.1 sign(a_(t)), and the derivative with respect to s_(t) is1[|s_(t)|>2] sign(s_(t)). In a situation in which s_(t)∈(1.5, 2], theadversarial choice of v_(t) would be to set v_(t)=0.5 and, therefore,there may be a non-zero loss on round t+1, whenever a_(t)>1.5−s_(t). Insuch cases, the derivative of the loss may back-propagate directly toa_(t). Thus, the adversarial choice of v_(t) may help the navigationalsystem obtain a non-zero back-propagation message in cases for which thechoice of at is sub-optimal. Such a relationship may aid thenavigational system in selecting present actions based on an expectationthat such a present action (even if that action would result in asuboptimal reward or even a loss) will provide opportunities in thefuture for more optimal actions that result in higher rewards.

Such an approach may be applied to virtually any navigational situationthat may arise. The following describes the approach applied to oneexample: adaptive cruise control (ACC). In the ACC problem, the hostvehicle may attempt to maintain an adequate distance to a target vehicleahead (e.g., 1.5 seconds to the target car). Another goal may be todrive as smooth as possible while maintaining the desired gap. A modelrepresenting this situation may be defined as follows. The state spaceis

³, and the action space is

. The first coordinate of the state is the speed of the target car, thesecond coordinate is the speed of the host vehicle, and the lastcoordinate is the distance between the host vehicle and target vehicle(e.g., location of the host vehicle minus the location of the targetalong the road curve). The action to be taken by the host vehicle is theacceleration, and may be denoted by a_(t). The quantity

may denote the difference in time between consecutive rounds. While

may be set to any suitable quantity, in target one example,

may be 0.1 seconds. Position, s_(t), may be denoted as s_(t)=(v_(t)^(tartget), v_(t) ^(host), x_(t)), and the (unknown) acceleration of thetarget vehicle may be denoted as a_(t) ^(target).

The full dynamics of the system can be described by:

v _(t) ^(target)=[v _(t−1) ^(target) +τa _(t−1) ^(target)]₊

v _(t) ^(host)=[v _(t−1) ^(host) +τa _(t−1)]₊

x _(t)=[x _(t−1)+τ(v _(t−1) ^(target) −v _(t−1) ^(host))]₊

This can be described as a sum of two vectors:

$\begin{matrix}{s_{\text{?}} = \left( {\left\lbrack {{s_{t - 1}\lbrack 0\rbrack} + {\tau\; a_{t - 1}^{target}}} \right\rbrack_{+},\left\lbrack {{s_{t - 1}\lbrack 1\rbrack} + {\tau\; a_{t - 1}}} \right\rbrack_{+},{+ \left\lbrack {{s_{t - 1}\lbrack 2\rbrack} +} \right.}} \right.} \\\left. \left. {\tau\;\left\lbrack {{s_{t - 1}\lbrack 0\rbrack} - {s_{t - 1}\lbrack 1\rbrack}} \right)} \right\rbrack_{+} \right) \\{= {\underset{\overset{\text{?}}{N}{({s_{\text{?} - 1},a_{\text{?}}})}}{\underset{︸}{\left( {{s_{t - 1}\lbrack 0\rbrack},\left\lbrack {{s_{t - 1}\lbrack 1\rbrack} + {\tau\; a_{t - 1}}} \right\rbrack_{+},\left\lbrack {{s_{t - 1}\lbrack 2\rbrack} + {\tau\left( {{s_{t - 1}\lbrack 0\rbrack} - {s_{t - 1}\lbrack 1\rbrack}} \right)}} \right\rbrack_{+}} \right)}} +}} \\{\underset{\text{?}}{\underset{︸}{\left( {\left\lbrack {{s_{t - 1}\lbrack 0\rbrack} + {\tau\; a_{t - 1}^{target}}} \right\rbrack_{+} - \left\lbrack {{s_{t - 1}\lbrack 0\rbrack} - \left( {{s_{t - 1}\lbrack 0\rbrack},0,0} \right)} \right.} \right.}}}\end{matrix}$ ?indicates text missing or illegible when filed

The first vector is the predictable part, and the second vector is theunpredictable part. The reward on round t is defined as follows:

−r _(t)=0.1|a _(t)|+[|x _(t) /x* _(t)−1|−0.3]₊ where x* _(t)=max{1,1.5v_(t) ^(host)}

The first term may result in a penalty for non-zero accelerations, thusencouraging smooth driving. The second term depends on the ratio betweenthe distance to the target car, x_(t), and the desired distance, x*_(t),which is defined as the maximum between a distance of 1 meter and breakdistance of 1.5 seconds. In some cases, this ratio may be exactly 1, butas long as this ratio is within [0.7, 1.3], the policy may forego anypenalties, which may allow the host vehicle some slack in navigation—acharacteristic that may be important in achieving a smooth drive.

Implementing the approach outlined above, the navigation system of thehost vehicle (e.g., through operation of driving policy module 803within processing unit 110 of the navigation system) may select anaction in response to an observed state. The selected action may bebased on analysis not only of rewards associated with the responsiveactions available relative to a sensed navigational state, but may alsobe based on consideration and analysis of future states, potentialactions in response to the futures states, and rewards associated withthe potential actions.

FIG. 16 illustrates an algorithmic approach to navigation based ondetection and long range planning. For example, at step 1601, the atleast one processing device 110 of the navigation system for the hostvehicle may receive a plurality of images. These images may capturescenes representative of an environment of the host vehicle and may besupplied by any of the image capture devices (e.g., cameras, sensors,etc.) described above. Analysis of one or more of these images at step1603 may enable the at least one processing device 110 to identify apresent navigational state associated with the host vehicle (asdescribed above).

At steps 1605, 1607, and 1609, various potential navigational actionsresponsive to the sensed navigational state may be determined. Thesepotential navigational actions (e.g., a first navigational actionthrough an Nth available navigational action) may be determined based onthe sensed state and the long range goals of the navigational system(e.g., to complete a merge, follow a lead vehicle smoothly, pass atarget vehicle, avoid an object in the roadway, slow for a detected stopsign, avoid a target vehicle cutting in, or any other navigationalaction that may advance the navigational goals of the system).

For each of the determined potential navigational actions, the systemmay determine an expected reward. The expected reward may be determinedaccording to any of the techniques described above and may includeanalysis of a particular potential action relative to one or more rewardfunctions. Expected rewards 1606, 1608, and 1610 may be determined foreach of the potential navigational actions (e.g., the first, second, andNth) determined in steps 1605, 1607, and 1609, respectively.

In some cases, the navigational system of the host vehicle may selectfrom among the available potential actions based on values associatedwith expected rewards 1606, 1608, and 1610 (or any other type ofindicator of an expected reward). For example, in some situations, theaction that yields the highest expected reward may be selected.

In other cases, especially where the navigation system engages in longrange planning to determine navigational actions for the host vehicle,the system may not choose the potential action that yields the highestexpected reward. Rather, the system may look to the future to analyzewhether there may be opportunities for realizing higher rewards later iflower reward actions are selected in response to a current navigationalstate. For example, for any or all of the potential actions determinedat steps 1605, 1607, and 1609, a future state may be determined. Eachfuture state, determined at steps 1613, 1615, and 1617, may represent afuture navigational state expected to result based on the currentnavigational state as modified by a respective potential action (e.g.,the potential actions determined at steps 1605, 1607, and 1609).

For each of the future states predicted at steps 1613, 1615, and 1617,one or more future actions (as navigational options available inresponse to determined future state) may be determined and evaluated. Atsteps 1619, 1621, and 1623, for example, values or any other type ofindicator of expected rewards associated with one or more of the futureactions may be developed (e.g., based on one or more reward functions).The expected rewards associated with the one or more future actions maybe evaluated by comparing values of reward functions associated witheach future action or by comparing any other indicators associated withthe expected rewards.

At step 1625, the navigational system for the host vehicle may select anavigational action for the host vehicle based on a comparison ofexpected rewards, not just based on the potential actions identifiedrelative to a current navigational state (e.g., at steps 1605, 1607, and1609), but also based on expected rewards determined as a result ofpotential future actions available in response to predicted futurestates (e.g., determined at steps 1613, 1615, and 1617). The selectionat step 1625 may be based on the options and rewards analysis performedat steps 1619, 1621, and 1623.

The selection of a navigational action at step 1625 may be based on acomparison of expected rewards associated with future action optionsonly. In such a case, the navigational system may select an action tothe current state based solely on a comparison of expected rewardsresulting from actions to potential future navigational states. Forexample, the system may select the potential action identified at step1605, 1607, or 1609 that is associated with a highest future rewardvalue as determined through analysis at steps 1619, 1621, and 1623.

The selection of a navigational action at step 1625 may also be based oncomparison of current action options only (as noted above). In thissituation, the navigational system may select the potential actionidentified at step 1605, 1607, or 1609 that is associated with a highestexpected reward, 1606, 1608, or 1610. Such a selection may be performedwith little or no consideration of future navigational states or futureexpected rewards to navigational actions available in response toexpected future navigational states.

On the other hand, in some cases, the selection of a navigational actionat step 1625 may be based on a comparison of expected rewards associatedwith both future action options and with current action options. This,in fact, may be one of the principles of navigation based on long rangeplanning. For example, expected rewards to future actions may beanalyzed to determine if any may warrant a selection of a lower rewardaction in response to the current navigational state in order to achievea potential higher reward in response to a subsequent navigationalaction expected to be available in response to future navigationalstates. As an example, a value or other indicator of an expected reward1606 may indicate a highest expected reward from among rewards 1606,1608, and 1610. On the other hand, expected reward 1608 may indicate alowest expected reward from among rewards 1606, 1608, and 1610. Ratherthan simply selecting the potential action determined at step 1605(i.e., the action giving rise to the highest expected reward 1606),analysis of future states, potential future actions, and future rewardsmay be used in making a navigational action selection at step 1625. Inone example, it may be determined that a reward identified at step 1621(in response to at least one future action to a future state determinedat step 1615 based on the second potential action determined at step1607) may be higher than expected reward 1606. Based on this comparison,the second potential action determined at step 1607 may be selectedrather than the first potential action determined at step 1605 despiteexpected reward 1606 being higher than expected reward 1608. In oneexample, the potential navigational action determined at step 1605 mayinclude a merge in front of a detected target vehicle, while thepotential navigational action determined at step 1607 may include amerge behind the target vehicle. While the expected reward 1606 ofmerging in front of the target vehicle may be higher than the expectedreward 1608 associated with merging behind the target vehicle, it may bedetermined that merging behind the target vehicle may result in a futurestate for which there may be action options yielding even higherpotential rewards than expected reward 1606, 1608, or other rewardsbased on available actions in response to a current, sensed navigationalstate.

Selection from among potential actions at step 1625 may be based on anysuitable comparison of expected rewards (or any other metric orindicator of benefits associated with one potential action overanother). In some cases, as described above, a second potential actionmay be selected over a first potential action if the second potentialaction is projected to provide at least one future action associatedwith an expected reward higher than a reward associated with the firstpotential action. In other cases, more complex comparisons may beemployed. For example, rewards associated with action options inresponse to projected future states may be compared to more than oneexpected reward associated with a determined potential action.

In some scenarios, actions and expected rewards based on projectedfuture states may affect selection of a potential action to a currentstate if at least one of the future actions is expected to yield areward higher than any of the rewards expected as a result of thepotential actions to a current state (e.g., expected rewards 1606, 1608,1610, etc.). In some cases, the future action option that yields thehighest expected reward (e.g., from among the expected rewardsassociated with potential actions to a sensed current state as well asfrom among expected rewards associated with potential future actionoptions relative to potential future navigational states) may be used asa guide for selection of a potential action to a current navigationalstate. That is, after identifying a future action option yielding thehighest expected reward (or a reward above a predetermined threshold,etc.), the potential action that would lead to the future stateassociated with the identified future action yielding the highestexpected reward may be selected at step 1625.

In other cases, selection of available actions may be made based ondetermined differences between expected rewards. For example, a secondpotential action determined at step 1607 may be selected if a differencebetween an expected reward associated with a future action determined atstep 1621 and expected reward 1606 is greater than a difference betweenexpected reward 1608 and expected reward 1606 (assuming + signdifferences). In another example, a second potential action determinedat step 1607 may be selected if a difference between an expected rewardassociated with a future action determined at step 1621 and an expectedreward associated with a future action determined at step 1619 isgreater than a difference between expected reward 1608 and expectedreward 1606.

Several examples have been described for selecting from among potentialactions to a current navigational state. Any other suitable comparisontechnique or criteria, however, may be used for selecting an availableaction through long range planning based on action and reward analysisextending to projected future states. Additionally, while FIG. 16represents two layers in the long range planning analysis (e.g., a firstlayer considering the rewards resulting from potential actions to acurrent state, and a second layer considering the rewards resulting fromfuture action options in response to projected future states), analysisbased on more layers may be possible. For example, rather than basingthe long range planning analysis upon one or two layers, three, four ormore layers of analysis could be used in selecting from among availablepotential actions in response to a current navigational state.

After a selection is made from among potential actions in response to asensed navigational state, at step 1627, the at least one processor maycause at least one adjustment of a navigational actuator of the hostvehicle in response to the selected potential navigational action. Thenavigational actuator may include any suitable device for controlling atleast one aspect of the host vehicle. For example, the navigationalactuator may include at least one of a steering mechanism, a brake, oran accelerator.

Navigation Based on Inferred Aggression of Others

Target vehicles may be monitored through analysis of an acquired imagestream to determine indicators of driving aggression. Aggression isdescribed herein as a qualitative or quantitative parameter, but othercharacteristics may be used: perceived level of attention (potentialimpairment of driver, distracted—cell phone, asleep, etc.). In somecases, a target vehicle may be deemed to have a defensive posture, andin some cases, the target vehicle may be determined to have a moreaggressive posture. Navigational actions may be selected or developedbased on indicators of aggression. For example, in some cases, therelative velocity, relative acceleration, increases in relativeacceleration, following distance, etc., relative to a host vehicle maybe tracked to determine if the target vehicle is aggressive ordefensive. If the target vehicle is determined to have a level ofaggression exceeding a threshold, for example, the host vehicle may beinclined to give way to the target vehicle. A level of aggression of thetarget vehicle may also be discerned based on a determined behavior ofthe target vehicle relative to one or more obstacles in a path of or ina vicinity of the target vehicle (e.g., a leading vehicle, obstacle inthe road, traffic light, etc.).

As an introduction to this concept, an example experiment will bedescribed with respect to a merger of the host vehicle into aroundabout, in which a navigational goal is to pass through and out ofthe roundabout. The situation may begin with the host vehicle approachesan entrance of the roundabout and may end with the host vehicle reachesan exit of the roundabout (e.g., the second exit). Success may bemeasured based on whether the host vehicle maintains a safe distancefrom all other vehicles at all times, whether the host vehicle finishesthe route as quickly as possible, and whether the host vehicle adheresto a smooth acceleration policy. In this illustration, N_(T) targetvehicles may be placed at random on the roundabout. To model a blend ofadversarial and typical behavior, with probability p, a target vehiclemay be modeled by an “aggressive” driving policy, such that theaggressive target vehicle accelerates when the host vehicle attempts tomerge in front of the target vehicle. With probability 1−p, the targetvehicle may be modeled by a “defensive” driving policy, such that thetarget vehicle decelerates and lets the host vehicle merge in. In thisexperiment, p=0.5, and the navigation system of the host vehicle may beprovided with no information about the type of the other drivers. Thetypes of other drivers may be chosen at random at the beginning of theepisode.

The navigational state may be represented as the velocity and locationof the host vehicle (the agent), and the locations, velocities, andaccelerations of the target vehicles. Maintaining target accelerationobservations may be important in order to differentiate betweenaggressive and defensive drivers based on the current state. All targetvehicles may move on a one-dimensional curve that outlines theroundabout path. The host vehicle may move on its own one-dimensionalcurve, which intersects the target vehicles' curve at the merging point,and this point is the origin of both curves. To model reasonabledriving, the absolute value of all vehicles' accelerations may be upperbounded by a constant. Velocities may also be passed through a ReLUbecause driving backward is not allowed. Note that by not allowingdriving backwards, long-term planning may become a necessity, as theagent cannot regret on its past actions.

As described above, the next state, s_(t+1), may be decomposed into asum of a predictable part, {circumflex over (N)}(s_(t),a_(t)), and anon-predictable part, v_(t). The expression, {circumflex over(N)}(s_(t),a_(t)), may represent the dynamics of vehicle locations andvelocities (which may be well-defined in a differentiable manner), whilev_(t) may represent the target vehicles' acceleration. It may beverified that N(s_(t),a_(t)) can be expressed as a combination of ReLUfunctions over an affine transformation, hence it is differentiable withrespect to s_(t) and a_(t). The vector v_(t) may be defined by asimulator in a non-differentiable manner, and may implement aggressivebehavior for some targets and defensive behavior for other targets. Twoframes from such a simulator are shown in FIGS. 17A and 17B. In thisexample experiment, a host vehicle 1701 learned to slowdown as itapproached the entrance of the roundabout. It also learned to give wayto aggressive vehicles (e.g., vehicles 1703 and 1705), and to safelycontinue when merging in front of defensive vehicles (e.g., vehicles1706, 1708, and 1710). In the example represented by FIGS. 17A and 17B,the navigation system of host vehicle 1701 is not provided with the typeof target vehicles. Rather, whether a particular vehicle is determinedto be aggressive or defensive is determined through inference based onobserved position and acceleration, for example, of the target vehicles.In FIG. 17A, based on position, velocity, and/or relative acceleration,host vehicle 1701 may determine that vehicle 1703 has an aggressivetendency and, therefore, host vehicle 1701 may stop and wait for targetvehicle 1703 to pass rather than attempting to merge in front of targetvehicle 1703. In FIG. 17B, however, target vehicle 1701 recognized thatthe target vehicle 1710 traveling behind vehicle 1703 exhibiteddefensive tendencies (again, based on observed position, velocity,and/or relative acceleration of vehicle 1710) and, therefore, completeda successful merge in front of target vehicle 1710 and behind targetvehicle 1703.

FIG. 18 provides a flowchart representing an example algorithm fornavigating a host vehicle based on predicted aggression of othervehicles. In the example of FIG. 18, a level of aggression associatedwith at least one target vehicle may be inferred based on observedbehavior of the target vehicle relative to an object in the environmentof the target vehicle. For example, at step 1801, at least oneprocessing device (e.g., processing device 110) of the host vehiclenavigation system may receive, from a camera associated with the hostvehicle, a plurality of images representative of an environment of thehost vehicle. At step 1803, analysis of one or more of the receivedimages may enable the at least one processor to identify a targetvehicle (e.g., vehicle 1703) in the environment of the host vehicle1701. At step 1805, analysis of one or more of the received images mayenable the at least one processing device to identify in the environmentof the host vehicle at least one obstacle to the target vehicle. Theobject may include debris in a roadway, a stoplight/traffic light, apedestrian, another vehicle (e.g., a vehicle traveling ahead of thetarget vehicle, a parked vehicle, etc.), a box in the roadway, a roadbarrier, a curb, or any other type of object that may be encountered inan environment of the host vehicle. At step 1807, analysis of one ormore of the received images may enable the at least one processingdevice to determine at least one navigational characteristic of thetarget vehicle relative to the at least one identified obstacle to thetarget vehicle.

Various navigational characteristics may be used to infer a level ofaggression of a detected target vehicle in order to develop anappropriate navigational response to the target vehicle. For example,such navigational characteristics may include a relative accelerationbetween the target vehicle and the at least one identified obstacle, adistance of the target vehicle from the obstacle (e.g., a followingdistance of the target vehicle behind another vehicle), and/or arelative velocity between the target vehicle and the obstacle, etc.

In some embodiments, the navigational characteristics of the targetvehicles may be determined based on outputs from sensors associated withthe host vehicle (e.g., radar, speed sensors, GPS, etc.). In some cases,however, the navigational characteristics of the target vehicles may bedetermined partially or fully based on analysis of images of anenvironment of the host vehicle. For example, image analysis techniquesdescribed above and in, for example, U.S. Pat. No. 9,168,868, which isincorporated herein by reference, may be used to recognize targetvehicles within an environment of the host vehicle. And, monitoring alocation of a target vehicle in the captured images over time and/ormonitoring locations in the captured images of one or more featuresassociated with the target vehicle (e.g., tail lights, head lights,bumper, wheels, etc.) may enable a determination of relative distances,velocities, and/or accelerations between the target vehicles and thehost vehicle or between the target vehicles and one or more otherobjects in an environment of the host vehicle.

An aggression level of an identified target vehicle may be inferred fromany suitable observed navigational characteristic of the target vehicleor any combination of observed navigational characteristics. Forexample, a determination of aggressiveness may be made based on anyobserved characteristic and one or more predetermined threshold levelsor any other suitable qualitative or quantitative analysis. In someembodiments, a target vehicle may be deemed as aggressive if the targetvehicle is observed to be following the host vehicle or another vehicleat a distance less than a predetermined aggressive distance threshold.On the other hand, a target vehicle observed to be following the hostvehicle or another vehicle at a distance greater than a predetermineddefensive distance threshold may be deemed defensive. The predeterminedaggressive distance threshold need not be the same as the predetermineddefensive distance threshold. Additionally, either or both of thepredetermined aggressive distance threshold and the predetermineddefensive distance threshold may include a range of values, rather thana bright line value. Further, neither of the predetermined aggressivedistance threshold nor the predetermined defensive distance thresholdmust be fixed. Rather these values, or ranges of values, may shift overtime, and different thresholds/ranges of threshold values may be appliedbased on observed characteristics of a target vehicle. For example, thethresholds applied may depend on one or more other characteristics ofthe target vehicle. Higher observed relative velocities and/oraccelerations may warrant application of larger threshold values/ranges.Conversely, lower relative velocities and/or accelerations, includingzero relative velocities and/or accelerations, may warrant applicationof smaller distance threshold values/ranges in making theaggressive/defensive inference.

The aggressive/defensive inference may also be based on relativevelocity and/or relative acceleration thresholds. A target vehicle maybe deemed aggressive if its observed relative velocity and/or itsrelative acceleration with respect to another vehicle exceeds apredetermined level or range. A target vehicle may be deemed defensiveif its observed relative velocity and/or its relative acceleration withrespect to another vehicle falls below a predetermined level or range.

While the aggressive/defensive determination may be made based on anyobserved navigational characteristic alone, the determination may alsodepend on any combination of observed characteristics. For example, asnoted above, in some cases, a target vehicle may be deemed aggressivebased solely on an observation that it is following another vehicle at adistance below a certain threshold or range. In other cases, however,the target vehicle may be deemed aggressive if it both follows anothervehicle at less than a predetermined amount (which may be the same as ordifferent than the threshold applied where the determination is based ondistance alone) and has a relative velocity and/or a relativeacceleration of greater than a predetermined amount or range. Similarly,a target vehicle may be deemed defensive based solely on an observationthat it is following another vehicle at a distance greater than acertain threshold or range. In other cases, however, the target vehiclemay be deemed defensive if it both follows another vehicle at greaterthan a predetermined amount (which may be the same as or different thanthe threshold applied where the determination is based on distancealone) and has a relative velocity and/or a relative acceleration ofless than a predetermined amount or range. System 100 may make anaggressive/defensive if, for example, a vehicle exceeds 0.5Gacceleration or deceleration (e.g., jerk 5 m/s3), a vehicle has alateral acceleration of 0.5G in a lane change or on a curve, a vehiclecauses another vehicle to do any of the above, a vehicle changes lanesand causes another vehicle to give way by more than 0.3G deceleration orjerk of 3 m/s3, and/or a vehicle changes two lanes without stopping.

It should be understood that references to a quantity exceeding a rangemay indicate that the quantity either exceeds all values associated withthe range or falls within the range. Similarly, references to a quantityfalling below a range may indicate that the quantity either falls belowall values associated with the range or falls within the range.Additionally, while the examples described for making anaggressive/defensive inference are described with respect to distance,relative acceleration, and relative velocity, any other suitablequantities may be used. For example, a time to collision may calculationmay be used or any indirect indicator of distance, acceleration, and/orvelocity of the target vehicle. It should also be noted that while theexamples above focus on target vehicles relative to other vehicles, theaggressive/defensive inference may be made by observing the navigationalcharacteristics of a target vehicle relative to any other type ofobstacle (e.g., a pedestrian, road barrier, traffic light, debris,etc.).

Returning to the example shown in FIGS. 17A and 17B, as host vehicle1701 approaches the roundabout, the navigation system, including its atleast one processing device, may receive a stream of images from acamera associated with the host vehicle. Based on analysis of one ormore of the received images, any of target vehicles 1703, 1705, 1706,1708, and 1710 may be identified. Further, the navigation system mayanalyze the navigational characteristics of one or more of theidentified target vehicles. The navigation system may recognize that thegap between target vehicles 1703 and 1705 represents the firstopportunity for a potential merge into the roundabout. The navigationsystem may analyze target vehicle 1703 to determine indicators ofaggression associated with target vehicle 1703. If target vehicle 1703is deemed aggressive, then the host vehicle navigation system may chooseto give way to vehicle 1703 rather than merging in front of vehicle1703. On the other hand, if target vehicle 1703 is deemed defensive,then the host vehicle navigation system may attempt to complete a mergeaction ahead of vehicle 1703.

As host vehicle 1701 approaches the roundabout, the at least oneprocessing device of the navigation system may analyze the capturedimages to determine navigational characteristics associated with targetvehicle 1703. For example, based on the images, it may be determinedthat vehicle 1703 is following vehicle 1705 at a distance that providesa sufficient gap for the host vehicle 1701 to safely enter. Indeed, itmay be determined that vehicle 1703 is following vehicle 1705 by adistance that exceeds an aggressive distance threshold, and therefore,based on this information, the host vehicle navigation system may beinclined to identify target vehicle 1703 as defensive. In somesituations, however, more than one navigational characteristic of atarget vehicle may be analyzed in making the aggressive/defensivedetermination, as discussed above. Furthering the analysis, the hostvehicle navigation system may determine that, while target vehicle 1703is following at a non-aggressive distance behind target vehicle 1705,vehicle 1703 has a relative velocity and/or a relative acceleration withrespect to vehicle 1705 that exceeds one or more thresholds associatedwith aggressive behavior. Indeed, host vehicle 1701 may determine thattarget vehicle 1703 is accelerating relative to vehicle 1705 and closingthe gap that exists between vehicles 1703 and 1705. Based on furtheranalysis of the relative velocity, acceleration, and distance (and evena rate that the gap between vehicles 1703 and 1705 is closing), hostvehicle 1701 may determine that target vehicle 1703 is behavingaggressively. Thus, while there may be a sufficient gap into which hostvehicle may safely navigate, host vehicle 1701 may expect that a mergein front of target vehicle 1703 would result in an aggressivelynavigating vehicle directly behind the host vehicle. Further, targetvehicle 1703 may be expected, based on the observed behavior throughimage analysis or other sensor output, that target vehicle 1703 wouldcontinue accelerating toward host vehicle 1701 or continuing toward hostvehicle 1701 at a non-zero relative velocity if host vehicle 1701 was tomerge in front of vehicle 1703. Such a situation may be undesirable froma safety perspective and may also result in discomfort to passengers ofthe host vehicle. For such reasons, host vehicle 1701 may choose to giveway to vehicle 1703, as shown in FIG. 17B, and merge into the roundaboutbehind vehicle 1703 and in front of vehicle 1710, deemed defensive basedon analysis of one or more of its navigational characteristics.

Returning to FIG. 18, at step 1809, the at least one processing deviceof the navigation system of the host vehicle may determine, based on theidentified at least one navigational characteristic of the targetvehicle relative to the identified obstacle, a navigational action forthe host vehicle (e.g., merge in front of vehicle 1710 and behindvehicle 1703). To implement the navigational action (at step 1811), theat least one processing device may cause at least one adjustment of anavigational actuator of the host vehicle in response to the determinednavigational action. For example, a brake may be applied in order togive way to vehicle 1703 in FIG. 17A, and an accelerator may be appliedalong with steering of the wheels of the host vehicle in order to causethe host vehicle to enter the roundabout behind vehicle 1703, as shownif FIG. 17B.

As described in the examples above, navigation of the host vehicle maybe based on the navigational characteristics of a target vehiclerelative to another vehicle or object. Additionally, navigation of thehost vehicle may be based on navigational characteristics of the targetvehicle alone without a particular reference to another vehicle orobject. For example, at step 1807 of FIG. 18, analysis of a plurality ofimages captured from an environment of a host vehicle may enabledetermination of at least one navigational characteristic of anidentified target vehicle indicative of a level of aggression associatedwith the target vehicle. The navigational characteristic may include avelocity, acceleration, etc. that need not be referenced with respect toanother object or target vehicle in order to make anaggressive/defensive determination. For example, observed accelerationsand/or velocities associated with a target vehicle that exceed apredetermined threshold or fall within or exceed a range of values mayindicate aggressive behavior. Conversely, observed accelerations and/orvelocities associated with a target vehicle that fall below apredetermined threshold or fall within or exceed a range of values mayindicate defensive behavior.

Of course, in some instances the observed navigational characteristic(e.g., a location, distance, acceleration, etc.) may be referencedrelative to the host vehicle in order to make the aggressive/defensivedetermination. For example, an observed navigational characteristic ofthe target vehicle indicative of a level of aggression associated withthe target vehicle may include an increase in relative accelerationbetween the target vehicle and the host vehicle, a following distance ofthe target vehicle behind the host vehicle, a relative velocity betweenthe target vehicle and the host vehicle, etc.

Trajectory Selection for an Autonomous Vehicle

Any or all of the systems and navigation methodologies described abovemay operate by receiving various inputs (e.g., captured images, LIDARinputs, speed inputs, location inputs, various sensor inputs, etc.) todetermine a navigational scenario for a host vehicle. Based on thedetected navigational scenario, which may include detected pedestrians;detected target vehicles; detected objects; determined speeds anddirections of travel of detected target vehicles; detected roadsurfaces, road markings, road type, lane markings; and multiple otherobserved characteristics of an environment of the host vehicle, a targettrajectory may be determined for the host vehicle. The target trajectorymay further be based on consideration of various predetermined safetyconstraints, as discussed in more detail in the sections above. Itshould be noted that the target trajectory determination may occur at aperiodic rate. In some embodiments, target trajectory determination mayoccur several times per minute or several times per second.

Once the target trajectory is determined, the navigational processor(s)may control various actuators onboard the host vehicle (e.g., actuatorscontrol steering components, braking components, accelerationcomponents, etc.) to cause the host vehicle to navigate through theenvironment relative to the target trajectory. In some cases, an actualtrajectory for the host vehicle may exactly match the determined targettrajectory. In other cases, however, the actual trajectory for the hostvehicle may differ from the target trajectory (e.g., by some allowablevariance, etc.) along one or more segments of the target trajectory.

In some embodiments, a process for determining the target trajectory forthe host vehicle may involve only a single target trajectory generatedbased on the many detected characteristics of a navigational stateassociated with the host vehicle. In other embodiments, however, aprocess for determining the target trajectory for the host vehicle mayinvolve the generation of multiple potential target trajectories basedon the detected navigational state of the host vehicle. In such cases,the multiple potential target trajectories may be evaluated, and asingle target trajectory for navigating the host vehicle may be selectedfrom among the group of potential target trajectories. Such selection,which may be performed by a trained neural network, as described above,may be based on a host of factors or considerations (e.g., compliancewith one or more predetermined safety constraints, potential effects ofthe selected trajectory on future navigational states of the vehicle,comfort of the host vehicle passengers, acceleration levels, or manyother factors).

Because the process for generated a target trajectory for a host vehiclemay occur many times per second, systems that involve the generation andevaluation of multiple potential target trajectories may result insignificant computational overhead for a navigational system. This maybe especially true in systems that generate and evaluate 10, 100, 1000,or a million or more, potential trajectories for evaluation. Evaluatinglarger numbers of potential trajectories may enable selection of bettertarget trajectories that may enhance the performance of the hostvehicle. In some cases, however, the navigational processor(s) may beunable to complete all of the computations necessary to evaluate all (ora substantial number) of the potential trajectories generated by atrajectory planner before the next trajectory evaluation and selectioncycle is to begin (especially where the cycle times are less than asecond, less than 500 milliseconds, less than 100 milliseconds, lessthan 10 milliseconds, etc.).

In some embodiments, to reduce computational overhead and facilitatetrajectory selection for the host vehicle, the trajectory selection mayoccur in a two (or more)-stage process. For example, referring to FIG.19, a system architecture 1900 implemented by the one or morenavigational processors may include a navigation state sensing block1902. This block, for example, may receive, from a camera, a pluralityof images representative of an environment of the host vehicle.Additionally, this block may receive sensor inputs from various othersources (e.g., LIDAR, RADAR, GPS, speed sensors, accelerometers, etc.)in order to detect various aspects of the navigational state of the hostvehicle. Using the sensor information coupled with the captured images,the navigational processor(s) may analyze at least one of the capturedplurality of images to identify navigational state informationassociated with the host vehicle. Such navigational state informationmay include any aspect of a current navigational situation for the hostvehicle that may be detected or otherwise discerned through analysis ofthe captured images together with or separate from analysis of othersensor information. In some cases, the navigational state informationmay include information (e.g., position, size, location, speed,direction of travel, type, etc.) of various types of objects detectablevia analysis of the captured images and/or the available sensorinformation. Such detectable objects may include, for example, one ormore target vehicles in the environment of the host vehicle, one or morelane markings detected in the environment of the host vehicle, one ormore of a current direction of travel associated with the host vehicle,a current velocity of the host vehicle, or a current acceleration of thehost vehicle; one or more detected pedestrians in the environment of thehost vehicle, etc.

Based on the sensed navigational state acquired by block 1902, thisinformation may be provided to a trajectory planner 1906. Thisinformation may optionally also be provided to routing module 1904,which may influence the potential trajectories generated by thetrajectory planner 1906, for example, by adding a layer of informationrelating to a desired route for the host vehicle (e.g., a routeincluding a desired ending destination location, information relating toroad type, road traffic flow direction, traffic levels, etc.).Trajectory planner 1906 may determine one or more of the potentialtrajectories for the host vehicle based, at least in part, on plannedroute information received from routing block 1904. The planned routeinformation may be generated by routing block 1904 based on informationreceived from a user of the host vehicle. In some embodiments, the usedby block 1904 may be received from a mapping system external to the hostvehicle and may be combined with routing information input by a user ofthe host vehicle. For example, a user may request transport to aparticular destination, and one or more maps needed for planning a routeto that destination may be accessed in a database onboard the hostvehicle or may be downloaded from a database at a server locationremotely located relative to the host vehicle.

The trajectory planner may be responsible for generating one or morepotential trajectories for the host vehicle in view of the sensednavigational state. In some cases, as suggested above, trajectoryplanner 1906 may generate a plurality of potential trajectories for thehost vehicle, such as 10, 100, 1000, a million, or more potentialtrajectories. These potential trajectories may be provided to a trainedneural network (e.g., RL block 1908) as k potential trajectory optionsto consider or evaluate. Trajectory planner 1906 may implement one ormore planning algorithms that may create a set of desires based on thenavigation sensing state and the routing information. For example, in atwo-lane highway scenario, this set can be: (1) stay in lane with samespeed, (2) stay in lane, but slow down to keep a larger distance to thepreceding car, or (3) overtake. For each such set, a planning algorithmmay construct a trajectory (of a variable length). The output oftrajectory planner 1906, as noted, may be a set of potentialtrajectories generated by the planning algorithm(s).

As noted, one function of RL block 1908 may include evaluating the kpotential trajectories generated by trajectory planner 1906 andselecting one trajectory (e.g., a planned trajectory) for the hostvehicle. One goal of the RL module 1908 may be to decide between optionsunder uncertainty due to long planning time horizons. For example, ifthe host vehicle is driving behind a slow truck in a two lane highway,and the traffic in the other lanes is much faster than the host vehiclespeed, the host vehicle may need to first slow down to make room, thenaccelerate, and only then may perform a takeover maneuver to pass theslow truck. Such complex maneuvers may require a relatively long time tocomplete, and information on the other vehicles may not be available forsuch a long time horizon. Instead, the navigational processor(s) mayrely on RL module 1908 to determine that, statistically, when drivingbehind a slow truck, the host vehicle must first make room before atakeover.

After selection of a particular target trajectory, that trajectory maybe supplied to a control API 1914 for implementation. That is, based onthe planned trajectory selected by RL block 1908 from among the kpotential trajectories, the navigational processor(s) may determine oneor more navigational actions for the host vehicle that may beimplemented such that the host vehicle navigates (either exactly orwithin some acceptable variance) the trajectory selected by RL block1908. To implement the determined navigational actions, the navigationalprocessor(s), using control API 1914, for example, may cause at leastone adjustment of a navigational actuator of the host vehicle toimplement one or more planned navigational actions for the host vehicle.

As noted above, in some cases, RL block 1908 may be required to evaluatemany potential trajectories supplied by trajectory planner 1906 in orderto select one trajectory for implementation. And in some cases,especially where the periodic evaluation cycle is short (e.g., less thana second, 500 milliseconds, 100 milliseconds, 10 milliseconds, etc.),there may be insufficient time from a computational standpoint to fullyevaluate all of the potential trajectory options, which may number intothe thousands, millions, or more. Thus, in some cases, evaluation of thepotential trajectory options within RL block 1908 may be accomplished ina multi-stage process. As shown in FIG. 19, RL block 1908 may include,for example, an RL trajectory option screener 1910 and an RL trajectoryoption selector 1912. RL trajectory option screener 1910 and/or RLtrajectory option selector 1912 may use reinforcement learning or othersuitable training techniques. To reduce computational overhead,potentially reduce computation time, potentially reduce a total numberof computations, and/or to speed the trajectory selection function ofthe RL block 1908, RL trajectory option screener 1910 may operate as afilter of the potential trajectories provided by trajectory planner1906. For example, as a first screening process, RL trajectory optionscreener 1910 may perform a preliminary analysis relative to each of theplurality of potential trajectories provided by trajectory planner 1906.This preliminary analysis may be performed in order to cull out lesslikely trajectory candidates based on an analysis that may require lesscomputational resources than a more complete trajectory evaluation mayrequire.

The result of the preliminary analysis performed by RL trajectory optionscreener 1910 may be identification, for further consideration, of asubset of the potential trajectories provided by trajectory planner1906. Thus, rather than fully evaluating each and every potentialtrajectory generated by the trajectory planner 1906, RL trajectoryoption screener 1910 may select only a subset of those potentialtrajectories deemed appropriate for further evaluation by, for example,RL trajectory selector 1912. Such a reduction in the number of potentialtrajectories to be subjected to a complete evaluation process by RLtrajectory selector 1912 may speed the process for selection of a targettrajectory for the host vehicle and/or may free computational resourcesfor other tasks.

Before turning to potential techniques for selecting a subset ofpotential trajectories for further evaluation by RL trajectory selector1912, a secondary analysis performed by RL trajectory selector 1912 forselecting the target trajectory of the host vehicle will be described.RL trajectory selector 1912 may receive a subset of potentialtrajectories from RL trajectory option screener 1910. To select a targettrajectory for the host vehicle from among this subset, RL trajectoryselector 1912 may perform a secondary analysis (e.g., after thepreliminary analysis performed by RL trajectory option screener 1910 togenerate the subset from among the plurality of potential trajectoriesprovided by trajectory planner 1906) relative to the subset of theplurality of potential trajectories. Based on the secondary analysis, RLtrajectory selector 1912 may select one of the subset of the pluralityof potential trajectories as a planned/target trajectory for the hostvehicle.

Various techniques may be employed by RL trajectory selector 1912 forevaluating and selecting the target trajectory for the host vehicle. Inone example, a policy gradient may be used for option ranking in orderto select a particular target trajectory for the host vehicle from amongthe subset of available options provided by RL trajectory optionscreener 1910. To illustrate the policy gradient technique, consider thefollowing setting. At round t, a navigation state sensing module mayobserve a state, s_(t). There may be an “options generation function,”g, that receives as an input the state s_(t) and constructs an outputk_(t) options (e.g., from trajectory planner 1906) represented asvectors v_(t, 1), . . . , v_(t), k_(t). The trained system may choose anoption a_(t)∈[k_(t)]. It is possible that in some time stamps, thesystem may have k_(t)=1 (of course, in other situations k_(t) may bemore, even much more, than 1). For example, if in round t an optionrepresenting an “overtake” maneuver is chosen, from among the options{“stay,” “overtake}, it may be the case that in the next rounds therewill only exist the “overtake” option until the options generationfunction determines that the overtake has been completed or that itshould not proceed as planned. In these rounds, k_(t+1)=1. Afterperforming T steps, the agent may receive a reward, R(s₁, . . . s_(T))(for simplicity, the reward may be applied only over the states).

Because the number of options can vary, a network architecture may beconsidered that receives a pair (s, v), representing a state and arepresentation of an option as a vector, and outputs a score N_(θ)(s,v). Here, θ is the parameter vector of the network. At each step t, thisnetwork may be applied on some or all of the available options to getthe vector:

(N _(θ)(s _(t) ,v _(t,1)), . . . ,N _(θ)(s _(t) ,v _(t) ,k _(t))).

This vector may be passed through a soft-max layer to get a distributionπ_(θ)(⋅|s_(t)) over the k_(t) options. The policy π_(θ) induces aprobability distribution over sequences as follows: given a sequences=(s₁, a₁), . . . , (s_(T), a_(T)), we have:

${P_{\theta}\left( \overset{\_}{s} \right)} = {\prod\limits_{t = 1}^{T}\left\lbrack {s_{t}\left. s_{{1\text{:}t} - 1} \right\rbrack{\pi_{\theta}\left( a_{t} \right.}s_{t}} \right)}$

A goal may be to find θ that maximizes

s ~ ~ P s ⁡ ( s _ ) ⁡ [ R ⁡ ( s _ ) ]

Below is derived an expression for the gradient of this objective usinga likelihood ratio technique:

∇ θ ⁢ ? ~ ? ⁡ [ R ⁢ ( s _ ) ] = ⁢ ∇ θ ⁢ ∑ s ⁢ P θ ⁡ ( s _ ) ⁢ R ⁡ ( s _ ) = ⁢ ∑ s ⁢R ⁡ ( s _ ) ⁢ ∇ s ⁢ P s ⁡ ( s _ ) = ⁢ ∑ s ⁢ P θ ⁡ ( s _ ) ⁢ R ⁡ ( s _ ) ⁢ ∇ s ⁢ P s⁡( s _ ) P θ ⁡ ( s _ ) = ⁢ ∑ s ⁢ P θ ⁡ ( s _ ) ⁢ R ⁡ ( s _ ) ⁢ ∇ θ ⁢ log ⁡ ( P θ ⁡( s _ ) ) = ⁢ ∑ s ⁢ P θ ⁡ ( s _ ) ⁢ R ⁡ ( s _ ) ⁢ ∇ θ ⁢ ( ∑ t = 1 T ⁢ log ( [ st  ⁢ s _ 1 ⁢ : ⁢ t - 1 ] ) + ∑ t = 1 T ⁢ log ( π θ [ a t ⁢  s t ) ) ) = ⁢ ∑s ⁢ P θ ⁡ ( s _ ) ⁢ R ⁡ ( s _ ) ⁢ ( ∑ t = 1 T ⁢ ∇ θ ⁢ log ( [ s t  ⁢ s _ 1 ⁢ : ⁢t - 1 ] ) + ∑ t = 1 T ⁢ ∇ θ ⁢ log ( π θ [ a t ⁢  s t ) ) ) = ⁢ ∑ s ⁢ P s ⁡ (s _ ) ⁢ R ⁡ ( s _ ) ⁢ ( 0 + ∑ t = 1 T ⁢ ∇ θ ⁢ log ⁡ ( π θ ( a t  ⁢ s t ) ) ) ⁢= ⁢ ? ~ ? [ R ⁡ ( s _ ) ⁢ ∑ t = 1 T ⁢ ∇ θ ⁢ log ⁡ ( π θ ( a t  ⁢ s t ) ) ] . ⁢ ⁢( definition ⁢ ⁢ of ⁢ ⁢ expectation ) ⁢ ( linearity ⁢ ⁢ of ⁢ ⁢ devation ) ⁢ (multiply ⁢ ⁢ and ⁢ ⁢ divide ⁢ ⁢ by ⁢ ⁢ P 0 ⁡ ( s _ ) ) ⁢ ( derivative ⁢ ⁢ of ⁢ ⁢ the ⁢ ⁢log ) ⁢ ( def ⁢ ⁢ of ⁢ ⁢ P 0 ) ⁢ ( linearity ⁢ ⁢ of ⁢ ⁢ derivative )?indicates text missing or illegible when filed

This expression yields a Stochastic Gradient Ascent algorithm in anatural way: at each iteration, sample s using the current value of θand set an unbiased estimate of the gradient to be R(s)Σ_(t=1) ^(T)∇^(θ)log(π_(θ)(a_(t)|s_(t))).

The variance of the policy gradient estimator may grow with T. In somecases, no matter what the current value of θ is, for every round onwhich k_(t)=1, there may be ∇_(θ) log(π_(θ)(a_(t)|s_(t)))=0. Therefore,the variance grows with the number of rounds on which k_(t)>1. It may beexpected that this may be a quite small number even if T is very large.

One problem with a policy gradient may arise when there is a smalladvantage of choosing some action a over other possible actions. Forexample, suppose a policy gradient to choose between {“stay”,“overtake”}is applied, and the policy selects among these two options in everystep, for a time resolution of 10 Hz. In this case, it may not make abig difference if at time step t we choose “stay” or “overtake”, becausethe decision may change at time t+1. This is formalized by the so called“advantage” function (defined using the Q function). In some cases, ifmost of the times k_(t)=1, and only from time to time the system maymake the decision between {“stay”,“overtake”}, then there may be a bigadvantage between the two options, and therefore the gradient willcontain a stronger signal.

Returning to the operation of RL trajectory option screener 1910, thismodule, as noted, may pare down the trajectory options for RL trajectoryselection module 1912 to consider (e.g., using the policy gradienttechnique described above) to a subset of potential trajectories.Identification of this subset may proceed according to any suitabletechnique. In some embodiments, subset identification may involve apolicy gradient function as described above. In other embodiments,however, the subset identification process may involve one or more lesscomputationally intensive processes. For example, as ascreening/filtering process, RL trajectory option screener 1910 mayassign to each of the plurality of potential trajectories supplied bytrajectory planner 1906 at least one indicator of relative ranking thatcan be used to identify the subset of trajectories for providing to RLtrajectory selector 1912. Indeed, the subset of potential trajectoriesmay be selected by RL trajectory option screener 1910 based on the atleast one indicator of relative ranking assigned to each of theplurality of potential trajectories, wherein the subset of the pluralityof potential trajectories includes fewer potential trajectories than theplurality of potential trajectories.

The at least one indicator of relative ranking may take any formsuitable for distinguishing from among the potential trajectories andfor selecting a subset of the potential trajectories (e.g., by a trainedneural network). In some embodiments, the indicator of relative rankingmay include a value associated with a probability distribution acrossthe plurality of potential trajectories. For example, based on one ormore characteristics of the potential trajectories, a probabilitydistribution value (e.g., a number between 0 and 1), a numeric score, orany other suitable value may be assigned to each potential trajectory.In some cases, such values or scores may be used to identify certainpotential trajectories to be included in the subset provided to RLtrajectory selector 1912 and to identify other potential trajectories tobe excluded from the subset. For example, in some embodiments, theassigned values or scores may be compared to one or more predeterminedthresholds or may be used in one or more functions to select thepotential trajectories to be included in the subset provided to RLtrajectory selector 1912.

The probabilistic values and/or numeric scores associated with thepotential trajectories may be assigned based on any suitable methodologyor test. In some embodiments, the preliminary analysis performed to rankthe potential trajectories may include testing each of the plurality ofpotential trajectories relative to one or more predeterminednavigational constraints. Such a test may be performed, in many cases,more quickly than the type of evaluation performed by RL trajectoryselector 1912. If a potential trajectory is found to be in compliancewith a particular navigational constraint, it may be assigned a 1, forexample. If the potential trajectory would not comply with thenavigational constraint, it may be assigned a 0. In such an example, thesubset of potential trajectories provided to RL trajectory selector 1912may include only those potential trajectories assigned with 1 values.

Various navigational constraints may be used for this type ofevaluation. For example, in some embodiments, a navigational constraintused to evaluate a potential trajectory may include a safety constraint,such as a pedestrian envelope. The pedestrian envelope may define abuffer zone, within which navigation of the host vehicle is prohibited,and at least a portion of the buffer zone may extend a predetermineddistance from a detected pedestrian. If a potential trajectory wouldviolate a safety constraint, such as the pedestrian envelope, it may beeliminated from the subset of trajectories provided to RL trajectoryselector 1912. In such a case, its indicator of relative ranking wouldflow from its designation as a trajectory not in compliance with atleast one navigational constraint.

Other constraints may also be considered in evaluating the potentialtrajectories generated by trajectory planner 1906. For example, in someembodiments, a safety constraint used in the evaluation may include atarget vehicle envelope, where the target vehicle envelope defines abuffer zone, within which navigation of the host vehicle is prohibited,and where at least a portion of the buffer zone extends a predetermineddistance from a detected target vehicle. Another constraint that may beused in the evaluation may include a stationary object envelope, wherethe stationary object envelope defines a buffer zone, within whichnavigation of the host vehicle is prohibited, and where at least aportion of the buffer zone extends a predetermined distance from adetected stationary object. Such detected stationary objects mayinclude, for example, at least one of a tree, a pole, a road sign, or anobject in a roadway. In other cases, a navigation constraint used toevaluate potential trajectories may include a maximum acceleration rate(e.g., deceleration rate) for the host vehicle. For example, if thepotential trajectory would result in the violation of a maximumacceleration for the host vehicle imposed by a predetermined safetyconstraint, it may be excluded from the subset of trajectories passed onto RL trajectory selector 1912. Additionally, if the potentialtrajectory would result in the violation of a maximum acceleration forthe host vehicle imposed by a predetermined comfort constraint (e.g.,one not necessary to preserve safety, but rather to contribute to thecomfort of host vehicle passengers), it may be excluded from the subsetof trajectories passed on to RL trajectory selector 1912.

In some embodiments, RL trajectory option screener may evaluate thepotential trajectories relative to one or more reward functions. Basedon this evaluation, a numeric score (or other quantifier) may beassigned to each potential trajectory based on its reward generatedrelative to the reward function. In some cases, the potentialtrajectories included in the subset of trajectories provided to RLtrajectory selector 1912 may include only those achieving at least acertain score relative to the applied reward function (or functions).

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-28. (canceled)
 29. A navigation system for a host vehicle, thenavigation system comprising: at least one processor comprisingcircuitry and a memory, wherein the memory includes instructions thatwhen executed by the circuitry cause the at least one processor to:receive a plurality of images acquired by a camera, the plurality ofimages being representative of an environment of the host vehicle;analyze at least one of the plurality of images to determine a pluralityof navigational constraints based on the environment of the hostvehicle; determine a plurality of potential trajectories for the hostvehicle based on the navigational constraints, the navigationalconstraints being associated with relative priorities between thenavigational constraints; determine at least one navigational constraintrelaxation factor; based on the at least one navigational constraintrelaxation factor, relax at least one of the navigational constraints tobe less restrictive; based on the at least one relaxed navigationalconstraint and the relative priorities between the navigationalconstraints, select one of the plurality of potential trajectories as aplanned trajectory for the host vehicle; determine one or morenavigational actions for the host vehicle based on the plannedtrajectory selected from among the plurality of potential trajectories;and cause at least one adjustment of a navigational actuator of the hostvehicle to implement the one or more navigational actions for the hostvehicle.
 30. The navigation system of claim 29, wherein determining theat least one navigational constraint relaxation factor comprisesidentifying the at least one navigational constraint relaxation factorin the environment of the host vehicle.
 31. The navigation system ofclaim 29, wherein the at least one navigational relaxation factorcomprises an action or a facing direction of a pedestrian.
 32. Thenavigation system of claim 29, wherein the at least one relaxednavigational constraint comprises a buffer zone.
 33. The navigationsystem of claim 32, wherein relaxing the at least one of thenavigational constraints to be less restrictive comprises reducing awidth of the buffer zone.
 34. The navigation system of claim 29, whereinrelaxing the at least one of the navigational constraints to be lessrestrictive comprises adjusting at least one of: a vehicle speedconstraint, a vehicle acceleration constraint, or a vehicle decelerationconstraint.
 35. The navigation system of claim 29, wherein the at leastone navigational constraint relaxation factor comprises at least one of:a type of curb stone, a lack of pedestrians in the environment, or alack of objects in the environment.
 36. The navigation system of claim29, wherein the navigational constraints comprise at least one of: aminimum safe driving distance with respect to a pedestrian, a targetvehicle, a road barrier, or a detected object.
 37. The navigation systemof claim 29, wherein the relative priorities between the navigationalconstraints are based on safety risks associated with the navigationalconstraints.
 38. The navigation system of claim 29, wherein a first oneof the navigational constraints associated with a pedestrian isprioritized above a second one of the navigational constraintsassociated with a target vehicle separate from the host vehicle.
 39. Thenavigation system of claim 29, wherein the navigational actuator is asteering mechanism, a brake, or an accelerator.
 40. The navigationsystem of claim 29, wherein the at least one relaxed navigationalconstraint allows the host vehicle to navigate closer to a pedestrian.41. The navigation system of claim 29, wherein the navigation system isconfigured to analyze images of vehicle environments according to amachine learning process.
 42. The navigation system of claim 29, whereinthe memory includes instructions that when executed by the circuitrycause the at least one processor to receive planned route informationfor the host vehicle and determine the plurality of potentialtrajectories for the host vehicle based, at least in part, on theplanned route information.
 43. The navigation system of claim 29,wherein the potential trajectories are scored by a machine learningmodel; and the planned trajectory is selected based on the scoredpotential trajectories.
 44. The navigation system of claim 29, whereinthe at least one navigational constraint relaxation factor is determinedbased on information received by the host vehicle from a remote server.45. A non-transitory machine-readable medium including instructions,which, when executed by a processing device, cause the processing deviceto perform operations comprising: receiving a plurality of imagesacquired by a camera, the plurality of images being representative of anenvironment of the host vehicle; analyzing at least one of the pluralityof images to determine a plurality of navigational constraints based onthe environment of the host vehicle; determining a plurality ofpotential trajectories for the host vehicle based on the navigationalconstraints, the navigational constraints being associated with relativepriorities between the navigational constraints; determining at leastone navigational constraint relaxation factor; based on the at least onenavigational constraint relaxation factor, relaxing at least one of thenavigational constraints to be less restrictive; based on the at leastone relaxed navigational constraint and the relative priorities betweenthe navigational constraints, selecting one of the plurality ofpotential trajectories as a planned trajectory for the host vehicle;determining one or more navigational actions for the host vehicle basedon the planned trajectory selected from among the plurality of potentialtrajectories; and causing at least one adjustment of a navigationalactuator of the host vehicle to implement the one or more navigationalactions for the host vehicle.
 46. The non-transitory machine-readablemedium of claim 45, wherein relaxing the at least one of thenavigational constraints to be less restrictive comprises adjusting atleast one of: a vehicle speed constraint, a vehicle accelerationconstraint, or a vehicle deceleration constraint.
 47. The non-transitorymachine-readable medium of claim 45, wherein the navigationalconstraints comprise at least one of: a minimum safe driving distancewith respect to a pedestrian, a target vehicle, a road barrier, or adetected object.
 48. The non-transitory machine-readable medium of claim45, wherein the relative priorities between the navigational constraintsare based on safety risks associated with the navigational constraints.49. The non-transitory machine-readable medium of claim 45, wherein thepotential trajectories are scored by a machine learning model; and theplanned trajectory is selected based on the scored potentialtrajectories.